BACKGROUND:Health utility values are required for cost-utility analyses in breast cancer, yet EQ-5D-5L-based health utility estimates and validity evidence across clinically relevant health states remain limited. The objectives of this study were to estimate health utility values for pre-defined breast cancer health states using the EQ-5D-5L (EuroQoL 5-level) instrument and to investigate its construct validity in breast cancer. METHODS:This cross-sectional study included women with invasive breast cancer, who completed both EQ-5D-5L and the Edmonton Symptom Assessment System. Participants were classified into five pre-defined health states, considered relevant both to clinical practice and economic modeling. We used the Canadian EQ-5D-5L value set to calculate community-valued health utility scores for each health state. Additionally, we evaluated aspects of construct validity (known-group and convergent validity) of EQ-5D-5L in breast cancer. RESULTS:549 women were included; the mean age was 57 (SD 12) years. The mean EQ-5D-5L index score was 0.83 (SD 0.13; range 0.13 to 0.95), with a distribution skewed towards full health and a ceiling effect of 20%. The mean health utility value for early-stage breast cancer was 0.84 (95% CI 0.83-0.86) and for metastatic breast cancer was 0.78 (95% CI 0.76-0.81). This difference was 0.060 (lower 95% CI bound 0.036, slightly lower than the pre-specified minimum important difference of 0.037). Health utility values and ESAS scores met almost all pre-specified criteria for convergent validity. CONCLUSION:We generated a list of health utility values for five pre-defined breast cancer health states using EQ-5D-5L. Additional research is required to confirm the validity of EQ-5D-5L as an outcome measure in breast cancer.

Rationale: Neoadjuvant chemotherapy (NAC) is a key element of treatment for locally advanced breast cancer (LABC). Predicting the response of NAC for patients with LABC before initiating treatment would be valuable to customize therapies and ensure the delivery of effective care. Objective: Our objective was to develop predictive measures of tumor response to NAC prior to starting for LABC using machine learning and textural computed tomography (CT) features in different level of frequencies. Materials and Methods: A total of 851 textural biomarkers were determined from CT images and their wavelet coefficients for 117 patients with LABC to evaluate the response to NAC. A machine learning pipeline was designed to classify response to NAC treatment for patients with LABC. For training predictive models, three models including all features (wavelet and original image features), only wavelet and only original-image features were considered. We determined features from CT images in different level of frequencies using wavelet transform. Additionally, we conducted a comparison of feature selection methods including mRMR, Relief, Rref QR decomposition, nonnegative matrix factorization and perturbation theory feature selection techniques. Results: Of the 117 patients with LABC evaluated, 82 (70%) had clinical–pathological response to chemotherapy and 35 (30%) had no response to chemotherapy. The best performance for hold-out data splitting was obtained using the KNN classifier using the Top-5 features, which were obtained by mRMR, for all features (accuracy = 77%, specificity = 80%, sensitivity = 56%, and balanced-accuracy = 68%). Likewise, the best performance for leave-one-out data splitting could be obtained by the KNN classifier using the Top-5 features, which was obtained by mRMR, for all features (accuracy = 75%, specificity = 76%, sensitivity = 62%, and balanced-accuracy = 72%). Conclusions: The combination of original textural features and wavelet features results in a greater predictive accuracy of NAC response for LABC patients. This predictive model can be utilized to predict treatment outcomes prior to starting, and clinicians can use it as a recommender system to modify treatment.

Background: Anthracycline–taxane chemotherapy is the gold standard in high-risk breast cancer (BC), despite the potential risk of congestive heart failure (CHF). A suitable alternative for anthracycline-sparing chemotherapy is through the combination of docetaxel and cyclophosphamide (TC). Methods: Through a retrospective study of stage I-III HER2-negative BC, using administrative databases, we analyzed a total of 10,634 women treated with adjuvant chemotherapy in Ontario, Canada, between 2009 and 2017. We compared TC versus standardized anthracycline–taxane chemotherapies (ACT and FEC-D). We investigated the overall survival (OS), and explored the incidence of CHF, emergency department (ED) visits and febrile neutropenia. Results: With a median follow-up of 5.5 years, the 5-year analysis showed an increased OS in patients treated with TC, versus those treated with ACT, HR 0.77 (0.63–0.95, p = 0.015). Among ER+ BC, there was an increased OS in patients treated with ACT and FEC-D, versus those treated with TC, HR 0.70 (0.52–0.95, p = 0.021) and HR 0.71 (0.56–0.91, p = 0.007), respectively. There were no substantial differences in CHF, between TC and anthracycline-based treatments. Patients treated with TC and FEC-D had more ED visits, compared to those treated with ACT. Conclusion: Our study shows that anthracycline–taxane regimens were the most commonly prescribed adjuvant chemotherapy options in HER2-negative BC. Women who received ACT had the lowest OS, likely due to their unfavorable pathology.

Quantitative ultrasound (QUS) detects early tumor microstructural changes during neoadjuvant chemotherapy (NAC), enabling personalized treatment adaptation. This study assessed the accuracy of machine learning models using serial QUS data to predict treatment response and evaluated their feasibility for guiding treatment personalization. This single-center, phase 2 randomized controlled trial (clinicaltrials.gov NCT04050228, Dec/2019) enrolled stage II-III breast cancer patients planned for standard NAC. QUS imaging was performed at baseline and week 4, with the latter used for response prediction. Patients were randomized 1:1 to standard or experimental arms, stratified by hormone receptor status. In the standard arm, oncologists were blinded to QUS results. In the experimental arm, predictions were disclosed to allow treatment modification at week 4. Final response was determined histopathologically (>30% tumor reduction or <5% cellularity). Between June 2018 and September 2023, 146 patients were enrolled, and 120 randomized (standard: 57, experimental: 63). Response rates were 93.0% (standard) and 96.8% (experimental). The model achieved 92% accuracy, 83% sensitivity, 93% specificity, and 99% positive predictive value. In the experimental arm, 8/63 patients were predicted non-responders, with 4 undergoing treatment modification. QUS-based machine learning enables accurate early response prediction and supports adaptive treatment strategies in future trials.

Background: Symptom burden and functional impairment are common in women with breast cancer, yet their prevalence and clinical significance across the disease spectrum remain underexplored. We sought to describe symptom burden and performance status using patient-reported outcome measures and to identify patient characteristics associated with symptoms requiring clinical intervention. Methods: In this cross-sectional study, women with stage I–IV breast cancer completed the Edmonton Symptom Assessment System (ESAS) and the Patient-Reported Functional Status tool. We assessed the prevalence and severity of symptoms and calculated summary distress scores. Multivariable logistic regression was used to identify patient characteristics associated with clinically significant symptoms (ESAS ≥ 4). Results: Among 381 women (mean age 56.8 years; 27% metastatic; 72% with no comorbidities), 70% reported at least one moderate to severe symptom. The most common were tiredness (31%), lack of well-being (30%), and anxiety (21%). Mean summary distress scores were low overall. Most patients reported functional status scores of 0 or 1, and 43% of those with scores ≥2 had metastatic disease. Compared with metastatic patients, women within the first year after diagnosis were less likely to report a symptom requiring intervention (OR 0.49, 95% CI 0.24–0.90). Conclusions: Clinically significant symptoms are common among women with breast cancer, including those with potentially curable disease. Threshold-based use of ESAS, rather than reliance on mean scores, provides a more accurate assessment of patient needs. These findings support the routine integration of patient-reported outcomes into oncology care and underscore the importance of targeted multidisciplinary interventions.

Objective The Canadian Real-world Evidence for Value in Cancer (CanREValue) Collaboration was established in response to growing interest in using real-world evidence (RWE) to support health technology assessment (HTA). CanREValue has developed a framework to generate and use RWE to inform cancer drug funding decisions.Design and participants The RWE framework was developed using a multistage, multistakeholder approach. First, an environmental scan and qualitative study were conducted to understand the current state and key stakeholder perspectives on RWE. Next, five formal working groups (WGs) were established consisting of stakeholders with cancer drug funding expertise including clinicians, patients, methodologists, payers, regulatory decision-makers and data analysts. Through stakeholder consultations, including modified Delphi exercises and workshops, each WG developed specific framework components and identified facilitators and barriers that may impact the uptake of RWE.Setting The CanREValue Collaboration consisted of membership and participation from stakeholders and expertise from across Canada. Central research operations were managed from Toronto, Ontario, Canada.Outcomes Development of an RWE framework reflective of the needs and perspectives of stakeholders directly involved and/or impacted by cancer drug funding decisions across Canada.Results Through an iterative process, a comprehensive RWE framework was developed that outlined the end-to-end processes necessary for the generation and use of RWE for HTA reassessment in Canada. The framework consists of four phases that uses various tools, templates and processes, which can be applied as a whole or in part. A diverse range of stakeholders and expertise is involved in the decision-making of each phase of the process: Phase I: identification, selection and prioritisation of RWE questions; phase II: initiating and planning the RWE study; phase III: conducting the RWE study and phase IV: conducting reassessment.Conclusions As the cancer drug funding landscape continues to evolve, the need for RWE to support evidence-based policy reform, pricing and reallocation of funding from low to high value settings is crucial. We have developed a framework that is adaptable and responsive to the changing landscape. The tools, templates and processes within the framework can be applied by various stakeholder groups in whole or in part to support cancer drug funding decision-making in Canada and can be adapted for use in other jurisdictions.

Background/Objectives: Patients with breast cancer who do not achieve a complete response to neoadjuvant chemotherapy (NAC) may benefit from intensified adjuvant systemic therapy. However, such treatment escalation is typically delayed until after tumour resection, which occurs several months into the treatment course. Quantitative ultrasound (QUS) can detect early microstructural changes in tumours and may enable timely identification of non-responders during NAC, allowing for earlier treatment intensification. In our previous prospective observational study, 100 breast cancer patients underwent QUS imaging before and four times during NAC. Machine learning algorithms based on QUS texture features acquired in the first week of treatment were developed and achieved 78% accuracy in predicting treatment response. In the current study, we aimed to validate these algorithms in an independent prospective cohort to assess reproducibility and confirm their clinical utility. Methods: We included breast cancer patients eligible for NAC per standard of care, with tumours larger than 1.5 cm. QUS imaging was acquired at baseline and during the first week of treatment. Tumour response was defined as a ≥30% reduction in target lesion size on the resection specimen compared to baseline imaging. Results: A total of 51 patients treated between 2018 and 2021 were included (median age 49 years; median tumour size 3.6 cm). Most were estrogen receptor–positive (65%) or HER2-positive (33%), and the majority received dose-dense AC-T (n = 34, 67%) or FEC-D (n = 15, 29%) chemotherapy, with or without trastuzumab. The support vector machine algorithm achieved an area under the curve of 0.71, with 86% accuracy, 91% specificity, 50% sensitivity, 93% negative predictive value, and 43% positive predictive value for predicting treatment response. Misclassifications were primarily associated with poorly defined tumours and difficulties in accurately identifying the region of interest. Conclusions: Our findings validate QUS-based machine learning models for early prediction of chemotherapy response and support their potential as non-invasive tools for treatment personalization and clinical trial development focused on early treatment intensification.

This work was conducted in order to validate a pre-treatment quantitative ultrasound (QUS) and texture derivative analyses-based prediction model proposed in our previous study to identify responders and non-responders to neoadjuvant chemotherapy in patients with breast cancer. The validation cohort consisted of 56 breast cancer patients diagnosed between the years 2018 and 2021. Among all patients, 53 were treated with neoadjuvant chemotherapy and three had unplanned changes in their chemotherapy cycles. Radio Frequency (RF) data were collected volumetrically prior to the start of chemotherapy. In addition to tumour region (core), a 5 mm tumour-margin was also chosen for parameters estimation. The prediction model, which was developed previously based on quantitative ultrasound, texture derivative, and tumour molecular subtypes, was used to identify responders and non-responders. The actual response, which was determined by clinical and pathological assessment after lumpectomy or mastectomy, was then compared to the predicted response. The sensitivity, specificity, positive predictive value, negative predictive value, and F1 score for determining chemotherapy response of all patients in the validation cohort were 94%, 67%, 96%, 57%, and 95%, respectively. Removing patients who had unplanned changes in their chemotherapy resulted in a sensitivity, specificity, positive predictive value, negative predictive value, and F1 score of all patients in the validation cohort of 94%, 100%, 100%, 50%, and 97%, respectively. Explanations for the misclassified cases included unplanned modifications made to the type of chemotherapy during treatment, inherent limitations of the predictive model, presence of DCIS in tumour structure, and an ill-defined tumour border in a minority of cases. Validation of a model was conducted in an independent cohort of patient for the first time to predict the tumour response to neoadjuvant chemotherapy using quantitative ultrasound, texture derivate, and molecular features in patients with breast cancer. Further research is needed to improve the positive predictive value and evaluate whether the treatment outcome can be improved in predicted non-responders by switching to other treatment options.

BACKGROUND:Immunotherapy in the presence of COVID-19 infections raises concerns because of potential overlapping clinical complications and immune system enhancement. Further investigation is warranted to establish its safety and to improve clinical decisions. METHODS:We conducted a retrospective cohort study using linked health administrative data from Ontario, Canada to assess 30-day mortality in patients with solid tumors who were treated with immunotherapy within 120 days before testing positive for COVID-19. A stepwise multivariable logistic regression model was used to identify clinical factors associated with 30-day mortality. RESULTS:Between January 2020 and April 2023, 281 patients tested positive for COVID-19 and were included in our study. The mean age was 68 (Standard Deviation: 10.3), 45% (127/281) were females and 58% (163/281) had lung cancer. 59% of patients (167/281) were treated with single agent immunotherapy, and almost 80% received at least one dose of COVID-19 vaccine. The 30-day mortality was 22% (63/281) and < 5% of patients were admitted to ICU or required ventilation. Factors associated with higher mortality were older age (Odds Ratio (OR) 1.60, 95% confidence interval (CI) 1.07-2.39), prior radiation therapy (OR 2.38, 95%CI 1.08-5.28), lower hemoglobin (< 10 g/dl) (OR 4.08, 95%CI 1.89-8.82) and higher leucocytes count (> 11,000/mm3) (OR 3.63, 95%CI 1.55-8.52). CONCLUSIONS:Immunotherapy does not seem to increase the risk of 30-day mortality in patients with COVID-19 infections compared to published outcomes of patients with cancer and COVID-19. Mortality was associated with certain clinical characteristics that need to be carefully examined when prescribing immunotherapy during future comparable pandemics.

BackgroundIn patients with locally advanced breast cancer (LABC) receiving neoadjuvant chemotherapy (NAC), quantitative ultrasound (QUS) radiomics can predict final responses early within 4 of 16-18 weeks of treatment. The current study was planned to study the feasibility of a QUS-radiomics model-guided adaptive chemotherapy.MethodsThe phase 2 open-label randomized controlled trial included patients with LABC planned for NAC. Patients were randomly allocated in 1:1 ratio to a standard arm or experimental arm stratified by hormonal receptor status. All patients were planned for standard anthracycline and taxane-based NAC as decided by their medical oncologist. Patients underwent QUS imaging using a clinical ultrasound device before the initiation of NAC and after the 1st and 4th weeks of treatment. A support vector machine-based radiomics model developed from an earlier cohort of patients was used to predict treatment response at the 4th week of NAC. In the standard arm, patients continued to receive planned chemotherapy with the treating oncologists blinded to results. In the experimental arm, the QUS-based prediction was conveyed to the responsible oncologist, and any changes to the planned chemotherapy for predicted non-responders were made by the responsible oncologist. All patients underwent surgery following NAC, and the final response was evaluated based on histopathological examination.ResultsBetween June 2018 and July 2021, 60 patients were accrued in the study arm, with 28 patients in each arm available for final analysis. In patients without a change in chemotherapy regimen (53 of 56 patients total), the QUS-radiomics model at week 4 of NAC that was used demonstrated an accuracy of 97%, respectively, in predicting the final treatment response. Seven patients were predicted to be non-responders (observational arm (n=2), experimental arm (n=5)). Three of 5 non-responders in the experimental arm had chemotherapy regimens adapted with an early initiation of taxane therapy or chemotherapy intensification, or early surgery and ended up as responders on final evaluation.ConclusionThe study demonstrates the feasibility of QUS-radiomics adapted guided NAC for patients with breast cancer. The ability of a QUS-based model in the early prediction of treatment response was prospectively validated in the current study.Clinical trial registrationclinicaltrials.gov, ID NCT04050228.

Background: Anthracycline-taxane is the standard chemotherapy strategy for treating high-risk early breast cancer despite the potentially life-threatening adverse events caused by anthracyclines. Commonly, the combination of docetaxel and cyclophosphamide (TC) is considered an alternative option. However, the efficacy of TC compared to anthracycline-taxane chemotherapy is unclear. This study compares disease-free survival (DFS), overall survival (OS) and cardiotoxicity between adjuvant TC and anthracycline-taxane for stages I–III, HER2-negative breast cancer. Methods: A systematic search on MEDLINE, Embase and Cochrane CENTRAL for randomized-controlled trials published until 11 March 2024, yielded 203 studies with 11,803 patients, and seven trials were included. Results: TC results in little to no difference in DFS (HR 1.09, 95% CI 0.98–1.20; moderate-certainty of evidence); OS (1.02, 95% CI 0.89–1.16; high-certainty of evidence); and cardiotoxicity (RR 0.54, 95% CI 0.16–1.76; high-certainty of evidence), compared to anthracycline-taxane. In the subgroup analysis, patients with ≥4 lymph nodes had improved DFS from anthracycline-taxane over TC. Conclusions: Overall, there was no difference between TC and anthracycline-taxane in DFS, OS and cardiotoxicity. In women with ≥4 nodes, anthracycline-taxane was associated with a substantial reduction in relapse events, compared to TC. Our study supports the current standard of practice, which is to use anthracycline-taxane and TC chemotherapy as a reasonable option in select cases.

ObjectiveNeoadjuvant chemotherapy (NAC) is a key element of treatment for locally advanced breast cancer (LABC). Predicting the response to NAC for patients with Locally Advanced Breast Cancer (LABC) before treatment initiation could be beneficial to optimize therapy, ensuring the administration of effective treatments. The objective of the work here was to develop a predictive model to predict tumor response to NAC for LABC using deep learning networks and computed tomography (CT).Materials and methodsSeveral deep learning approaches were investigated including ViT transformer and VGG16, VGG19, ResNet-50, Res-Net-101, Res-Net-152, InceptionV3 and Xception transfer learning networks. These deep learning networks were applied on CT images to assess the response to NAC. Performance was evaluated based on balanced_accuracy, accuracy, sensitivity and specificity classification metrics. A ViT transformer was applied to utilize the attention mechanism in order to increase the weight of important part image which leads to better discrimination between classes.ResultsAmongst the 117 LABC patients studied, 82 (70%) had clinical-pathological response and 35 (30%) had no response to NAC. The ViT transformer obtained the best performance range (accuracy = 71 ± 3% to accuracy = 77 ± 4%, specificity = 86 ± 6% to specificity = 76 ± 3%, sensitivity = 56 ± 4% to sensitivity = 52 ± 4%, and balanced_accuracy=69 ± 3% to balanced_accuracy=69 ± 3%) depending on the split ratio of train-data and test-data. Xception network obtained the second best results (accuracy = 72 ± 4% to accuracy = 65 ± 4, specificity = 81 ± 6% to specificity = 73 ± 3%, sensitivity = 55 ± 4% to sensitivity = 52 ± 5%, and balanced_accuracy = 66 ± 5% to balanced_accuracy = 60 ± 4%). The worst results were obtained using VGG-16 transfer learning network.ConclusionDeep learning networks in conjunction with CT imaging are able to predict the tumor response to NAC for patients with LABC prior to start. A ViT transformer could obtain the best performance, which demonstrated the importance of attention mechanism.

In previously reported retrospective studies, high tumor RNA disruption during neoadjuvant chemotherapy predicted for post-treatment pathologic complete response (pCR) and improved disease-free survival at definitive surgery for primary early breast cancer. The BREVITY (Breast Cancer Response Evaluation for Individualized Therapy) prospective clinical trial (NCT03524430) seeks to validate these prior findings. Here we report training set (Phase I) findings, including determination of RNA disruption index (RDI) cut points for outcome prediction in the subsequent validation set (Phase II; 454 patients). In 80 patients of the training set, maximum tumor RDI values for biopsies obtained during neoadjuvant chemotherapy were significantly higher in pCR responders than in patients without pCR post-treatment (P = .008). Moreover, maximum tumor RDI values <= 3.7 during treatment predicted for a lack of pCR at surgery (negative predictive value = 93.3%). These findings support the prospect that on-treatment tumor RNA disruption assessments may effectively predict post-surgery outcome, possibly permitting treatment optimization.

Abstract Background: Despite the comparably comprehensive healthcare system in Canada, health inequities are widespread, and this holds true for breast cancer patients in Ontario. It is thus essential to learn if socio-demographic disparities continue after diagnosis of breast cancer, and if these contribute to higher mortality rates in the lower income quintile groups. Methods: We conducted a real-word population-based study using a provincial health administrative dataset from Ontario, Canada. We included patients diagnosed with HER2-negative breast cancer, treated with surgery and adjuvant chemotherapy, between 2009 to 2017. We used log-rank test and Kaplan Meier curves to compare overall survival (OS) between breast cancer populations among income quintile groups, and Cox regression to evaluate risk factors, using hazard ratio (HR) and 95% confidence intervals (CI). Results: We analysed 10,634 women diagnosed with stage I-III breast cancer. At diagnosis, in the Q1 group, there were 248 (15.8%) women with stage I, 997 (63.7%) with stage II, and 320 (20.45%) with stage III. In the Q5 group, there were 568 (22.15%), with stage I, 1,532 (59.75%) with stage II and 464 (18%) with stage III. Comparison between those in the lowest versus highest income quintile groups, and lymph node (LN) status at diagnosis, showed that in Q1, 534 (34%) women had LN 0 and 897 (57.3%) had LN+. In Q5, there were 954 (37.2%) women with LN 0 and 1,379 (53.8%) with LN+. Similarly, comparing tumor size (TS) at diagnosis, we found that in Q1, there were 463 (32.2%) women with TS≤ 2 cm and 974 (67.8%) with TS > 2 cm. In Q5, 915 (39%) women had TS≤ 2 cm and 1,426 (61%) had TS > 2 cm. In the Q1 group, there were 325 (20.7%) patients who received non- anthracycline and 1,240 (79.3%) treated with anthracycline-taxane chemotherapy. In Q5, there were 673 (26.2%) women treated with non-anthracycline and 1,891 (73.8%) who received anthracycline-taxane chemotherapy. The OS analysis based on the household income group compared to Q5 showed a substantial difference in the Q1 (HR 1.52, 1.2–1.9, p = 0.0002) and Q2 (HR 1.34, 1.1–1.7, p = 0.006) groups. In Cox regression models, Q5 group and endocrine receptor (ER) positive were significantly associated with reduced mortality risk. There was a significant correlation between increased risk of death and the following: receipt of non- anthracycline chemotherapy, TS > 2 cm, LN+ and grade 3 histology. Conclusion: Our study found an uneven distribution of breast cancer patients between the lowest and highest income quintile groups. Women with HER2-negative breast cancer who were part of the lower income quintile groups, thereby likely with a socioeconomic disadvantage, had the lowest OS. At diagnosis, these women were more likely to have more advanced breast cancer staging, including larger tumors, LN+, and receive anthracycline-based chemotherapy as compared to those with higher household income. Further research is warranted to identify the key social determinants that are systematically associated with disparities in equitable access to care. Citation Format: Danilo Giffoni M. M. Mata, Rossanna C. Pezo, Kelvin K.W. Chan, Ines Menjak, Andrea Eisen, Maureen Trudeau. Reduced survival outcomes in lower income quintile groups for women with breast cancer treated with chemotherapy in Ontario, Canada [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr B006.

Purpose A cross-sectional study was conducted to investigate the feasibility of implementing routine collection of the Euro-Qol 5 dimensions (EQ-5D) questionnaire, to inform drug and health technology reimbursement decision making.Methods Women with breast cancer were recruited during scheduled clinic visits to an academic cancer centre. EQ-5D-5L was self-administered using electronic tablets. Diagnostic and treatment data were abstracted from patient charts. Feasibility was assessed primarily by the proportion of patients who fully completed EQ-5D-5L and by their willingness to complete the instrument at each clinic visit.Results 588 women were approached for study participation, 341 were enrolled. Fully completed EQ-5D-5L questionnaires were obtained in 323 participants (95% of participants, 95% CI 92-97%). Median time for EQ-5D-5L completion was 1.5 minutes (range:0.35 to 14.7). Mean age of participants was 58 years old. Most women who completed EQ-5D were White, born outside Canada and presented a high education level; one-quarter had metastatic disease. Most participants reported "No problems" in all EQ-5D-5L dimensions. Mean EQ-5D-5L index and mean EQ-5D-5L VAS values for all participants were respectively 0.83 (SD 0.13) and 75.7 (SD 17.45), with patients with metastatic disease scoring the lowest values. Seventy-eight percent of participants were willing to complete EQ-5D-5L at each clinic visit; lower Charlson comorbidity index and higher education level were predictors of willingness to continue to answer EQ-5D-5L.Conclusions Tablet-based collection of EQ-5D-5L in the context of routine clinical practice proved to be feasible. However, many patients declined study participation or reported being in full health, raising concerns about whether this method of collecting EQ-5D adequately represents the health status of all breast cancer patients.

Multi-criteria decision analysis (MCDA) is a value assessment tool designed to help support complex decision-making by incorporating multiple factors and perspectives in a transparent, structured approach. We developed an MCDA rating tool, consisting of seven criteria evaluating the importance and feasibility of conducting potential real-world evidence (RWE) studies aimed at addressing uncertainties stemming from initial cancer drug funding recommendations. In collaboration with the Canadian Agency for Drugs and Technologies in Health’s Provincial Advisory Group, a validation exercise was conducted to further evaluate the application of the rating tool using RWE proposals varying in complexity. Through this exercise, we aimed to gain insight into consensus building and deliberation processes and to identify efficiencies in the application of the rating tool. An experienced facilitator led a multidisciplinary committee, consisting of 11 Canadian experts, through consensus building, deliberation, and prioritization. A total of nine RWE proposals were evaluated and prioritized as low (n = 4), medium (n = 3), or high (n = 2) priority. Through an iterative process, efficiencies and recommendations to improve the rating tool and associated procedures were identified. The refined MCDA rating tool can help decision-makers prioritize important and feasible RWE studies for research and can enable the use of RWE for the life-cycle evaluation of cancer drugs.

We investigated the impact of HER2-overexpression level on efficacy of neoadjuvant therapy (NAT) with trastuzumab in early breast cancer (BC). In our retrospective study of 161 patients, those with HER2-high versus HER2-intermediate disease were more likely to achieve pathological complete response after NAT. Importantly, patients with residual disease had lower disease-free survival, highlighting a need to develop personalized neoadjuvant strategies. Purpose: This study aimed to examine the impact of the level of HER2 overexpression on pathologic and clinical outcomes in HER2-positive breast cancer (BC) patients treated with neoadjuvant therapy (NAT). Methods: Women with Stage II or III HER2-positive BC who received anthracycline-taxane-trastuzumab NAT regimens followed by curativeintent surgery were included. Patients were classified according to tumor HER2 expression into HER2-high (immunohistochemistry (IHC) 3 + or fluorescence in situ hybridization (FISH) HER2/CEP17 ratio >5 or HER2 copy number >10) and HER2-intermediate (IHC 2 + with HER2/CEP17 ratio >2 to < 5 or copy number >4 to < 10). Univariate and multivariate logistic regression analyses were performed using HER2 expression as a categorical variable. The primary outcome was pathological complete response (pCR). Estimated 3-year disease-free survival (DFS) and Overall Survival (OS) were secondary outcomes. Results: Among 161 patients with HER2-positive BC, 139 (86%) and 22 (14%) were classified as HER2-high and HER2-intermediate, respectively; 105 (65.2%) had hormone receptor (HR)-positive tumors; 72 (45%) achieved a pCR. In the overall population, pCR rates of 18% and 49% were achieved in HER2-intermediate and HER2-high cases, respectively (odds ratio [OR] = 0.23 95% CI 0.07-0.72; P = .007). No pCRs were observed among HR-positive, HER2-intermediate cases. Estimated 3-year DFS was 97.1% versus 89.3% for patients achieving a pCR versus those with residual disease, respectively ( P = .0011). Conclusion: We found that patients with HER2-high disease were more likely to achieve pCR after NAT compared to patients with HER2-intermediate BC, a subgroup of patients that may benefit from more personalized NAT strategies.

BACKGROUND:There is no widely accepted framework to guide the development of condition-specific preference-based instruments (CSPBIs) that includes both de novo and from existing non-preference-based instruments. The purpose of this study was to address this gap by reviewing the published literature on CSPBIs, with particular attention to the application of item response theory (IRT) and Rasch analysis in their development.METHODS:A scoping review of the literature covering the concepts of all phases of CSPBI development and evaluation was performed from MEDLINE, Embase, PsychInfo, CINAHL, and the Cochrane Library, from inception to December 30, 2022.RESULTS:The titles and abstracts of 1,967 unique references were reviewed. After retrieving and reviewing 154 full-text articles, data were extracted from 109 articles, representing 41 CSPBIs covering 21 diseases or conditions. The development of CSPBIs was conceptualized as a 15-step framework, covering four phases: 1) develop initial questionnaire items (when no suitable non-preference-based instrument exists), 2) establish the dimensional structure, 3) reduce items per dimension, 4) value and model health state utilities. Thirty-nine instruments used a type of Rasch model and two instruments used IRT models in phase 3.CONCLUSION:We present an expanded framework that outlines the development of CSPBIs, both from existing non-preference-based instruments and de novo when no suitable non-preference-based instrument exists, using IRT and Rasch analysis. For items that fit the Rasch model, developers selected one item per dimension and explored item response level reduction. This framework will guide researchers who are developing or assessing CSPBIs.

Purpose: Vulnerable Elder Survey (VES-13) is a screening tool used in assessing older vulnerable patients at risk of functional decline. We sought to evaluate how VES-13 tool would impact oncologist referral pattern to geriatricians as our primary outcome. We also sought to better understand how VES-13 scores impacted referral to additional services (allied healthcare), and modification to oncological treatment. Methods: A retrospective review of VES-13 questionnaires completed by older women (age 70 or older) with breast cancer referred to the Senior Women’s Breast Cancer Clinic (SWBCC) was undertaken. Patients with a VES-13 score of three or greater, who were at significantly higher risk of functional decline, had further retrospective chart review for risk factors that would contribute to functional decline such as Eastern Cooperative Oncology Group (ECOG) score, social supports, and current living situation. The primary and secondary endpoints described above were analyzed through bivariate comparisons and multivariable logistical regression to determine if there was any statistical significance (p < 0.05). Results: 701 patients completed VES-13 form, of which 235 (33.5%) had a VES-13 score of three or greater. Less than 5% of oncologists documented VES-13 scores in their notes, with less than 5% of patients being referred for geriatric services. Neither VES-13 (p= 0.900) nor ECOG (p= 0.424) were associated with referral for geriatrics assessment. Referral to allied healthcare services was significantly associated with (ECOG) score (OR 2.24 [1.49-3.37], p < 0.0001), while not significantly associated with VES-13 score (OR 0.89 [0.78-1.02], p= 0.102). VES-13 (OR 1.23 [1.04-1.45], p=0.014) and ECOG (OR 2.37 [1.29-4.37), p=0.005) were both associated with modification in oncology treatment (chemotherapy or radiation). Conclusion: Approximately one third of our population was at risk of functional decline. VES-13 scores were infrequently mentioned in oncologists notes from their clinical assessments, with very few patients being referred for geriatric assessment. By not collecting and analyzing VES-13 scores, and relying on performance status alone, there is a missed opportunity in assessing for functional decline and reducing potential complications from treatment for our patients. Keywords: Allied healthcare professionals; Breast cancer; Frailty; Geriatric oncology; Geriatrics; Multidisciplinary; VES-13. Citation Format: Ewa F. Szumacher, Arman Zereshkian, Benazir Mir Khan Benazir khan, Xingshan Cao, Nayanee Henry=Noel, Ines Menjak, Rajin Mehta, Bonnie Bristow, Maureen Trudeau, Matthiew Neve, Mirelle Norris, Mark Pasetka, Katie Rice, Fiona McCullock, Frances Wright, Krista Dawdy. PD6-05 Retrospective analysis of VES-13 questionnaires in the Senior Women’s Breast Cancer Clinic at Sunnybrook Health Sciences, Toronto, Ontario, Canada [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr PD6-05.