Ellen Warner

Purpose Cancer-related cognitive impairment (CRCI) is a common concern among breast cancer (BC) patients receiving chemotherapy, yet predictors of short-term cognitive change remain unclear. This study examined whether baseline cardiorespiratory fitness (CRF) and moderate-to-vigorous physical activity (MVPA) moderate changes in objectively assessed cognitive function from pre- to post-chemotherapy in BC patients, compared with age-matched healthy controls. Methods This prospective study included assessments at baseline (pre-chemotherapy) and approximately a five-month follow-up (post-chemotherapy). Participants were BC patients with stage I–III disease initiating chemotherapy and age-matched healthy controls. Cognitive function was assessed using the NIH Remote Cognition Toolbox (episodic memory, working memory, language/reading, verbal learning). CRF was measured using a remotely administered 6-min walk test, and MVPA was assessed via accelerometry. Linear mixed-effects models tested group × time × predictor (baseline CRF or MVPA) effects, adjusting for anxiety, depression, and menopausal status. Results Fifty-five participants were included (BC: n = 32; controls: n = 23). Episodic memory improved over time in both groups (β = 4.69, p = 0.031). Higher baseline CRF was associated with better verbal learning (β = 0.02, p = 0.023), but neither CRF nor MVPA predicted cognitive change over time (all three-way interactions p ≥ 0.05). BC patients scored lower than controls at baseline on working memory and language/reading, while verbal learning improved more in BC patients. BC patients also showed greater declines in MVPA over time. Findings No short-term cognitive decline was observed from pre-chemotherapy to approximately 5 months post-chemotherapy. Higher baseline fitness and physical activity were associated with better cognitive performance but did not predict cognitive change. Longer follow-up is needed to clarify treatment-related cognitive trajectories.

Background: Cognitive impairments, a prevalent quality-of-life concern in breast cancer (BC), are particularly pronounced in women having undergone adjuvant chemotherapy. These impairments—affecting executive function, attention, and processing speed—are often underdiagnosed, with no established treatments. Exercise is a potential intervention to mitigate cancer-related cognitive impairment (CRCI). Since virtual care delivery is feasible, remotely delivered exercise interventions for CRCI management in patients with BC may be explored. Objective: We examined the feasibility of an 8-week remotely delivered combined exercise program (aerobic+resistance training) compared to a stretching and toning active control in postchemotherapy patients with BC. Methods: Patients with BC who completed adjuvant chemotherapy within 48 months were recruited across Canada from February to July 2023. The combined exercise group engaged in unsupervised aerobic exercise (30 minutes, thrice per week), supervised group-based resistance training (30 minutes, twice per week via Zoom [Zoom Communications, Inc]), and one recorded class weekly, supplemented with 4 biweekly behavioral counseling sessions. The active control group participated in low-intensity balance and flexibility classes (30 minutes, twice per week live, once per week recorded). Feasibility was assessed via enrollment, adherence, attrition, measurement completion, adverse events, and participant satisfaction; cognitive function, using the National Institutes of Health Toolbox Cognition Battery Remote Administration (V2) at baseline and post intervention. Results: Twenty-one participants (mean age 51.6, SD 7.2 years; 11.8, SD 12.9 months since treatment) were randomized to the combined exercise (n=10) or the active control (n=11) group. Final analyses included 18 participants (mean age 51.9, SD 7.4 years; mean months since treatment 12.6, SD 13.5) with 9 participants in each group (51.2% enrollment rate; 14.3% attrition; no adverse events). Measurement completion rates were 85.7% and participants reported high satisfaction with the intervention, indicating minimal burden. Adherence rates for exercise classes were 70.8% in both groups. Adherence to behavioral counseling sessions was 77.5% (combined exercise group only). There were no significant differences in objectively measured cognitive function, but small-to-medium effect size improvements were observed in objectively measured episodic memory (mean difference 5.33, 95% CI –12.5 to 23.2; ηp²=0.03), working memory (mean difference 8.17, 95% CI –4.2 to 20.6; ηp²=0.12), executive function updating (mean difference –394.35, 95% CI –1035.67 to 246.96 ms; ηp²=0.07), and immediate memory and verbal learning (mean difference +3.22, 95% CI –2.0 to 8.5; ηp²=0.12), trending toward the multicomponent exercise group vs the active control group. In contrast, small-to-medium effect size improvements were observed in the Oral Reading Recognition Test (mean difference –9.65, 95% CI –22.9 to 3.5; P=.14; ηp²=0.14) and Picture Vocabulary Test (mean difference –2.48, 95% CI –5.2 to 0.3, P=.07; ηp²=0.20), trending toward the active control group. Conclusions: A remotely delivered combined exercise intervention is feasible and may improve CRCI in patients with BC. Larger randomized controlled trials are warranted to confirm its efficacy in enhancing cognitive function and quality of life in this population. Trial Registration: ClinicalTrials.gov NCT05704855; https://clinicaltrials.gov/study/NCT05704855

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.

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.

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.

PurposeBreast density (BD) and background parenchymal enhancement (BPE) are important imaging biomarkers for breast cancer (BC) risk. We aim to evaluate longitudinal changes in quantitative BD and BPE in high-risk women undergoing dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI), focusing on the effects of age and transition into menopause.ApproachA retrospective cohort study analyzed 834 high-risk women undergoing breast DCE-MRI for screening between 2005 and 2020. Quantitative BD and BPE were derived using deep-learning segmentation. Linear mixed-effects models assessed longitudinal changes and the effects of age, menopausal status, weeks since the last menstrual period (LMP-wks), body mass index (BMI), and hormone replacement therapy (HRT) on these imaging biomarkers.ResultsBD decreased with age across all menopausal stages, whereas BPE declined with age in postmenopausal women but remained stable in premenopausal women. HRT elevated BPE in postmenopausal women. Perimenopausal women exhibited decreases in both BD and BPE during the menopausal transition, though cross-sectional age at menopause had no significant effect on either measure. Fibroglandular tissue was positively associated with BPE in perimenopausal women.ConclusionsWe highlight the dynamic impact of menopause on BD and BPE and correlate well with the known relationship between risk and age at menopause. These findings advance the understanding of imaging biomarkers in high-risk populations and may contribute to the development of improved risk assessment leading to personalized chemoprevention and BC screening recommendations.

BACKGROUND Cognitive impairments, a prevalent quality-of-life concern in breast cancer (BC), are particularly pronounced in women having undergone adjuvant chemotherapy. These impairments—affecting executive function, attention, and processing speed—are often underdiagnosed, with no established treatments. Exercise is a potential intervention to mitigate cancer-related cognitive impairment (CRCI). Since virtual care delivery is feasible, remotely delivered exercise interventions for CRCI management in patients with BC may be explored. OBJECTIVE We examined the feasibility of an 8-week remotely delivered combined exercise program (aerobic+resistance training) compared to a stretching and toning active control in postchemotherapy patients with BC. METHODS Patients with BC who completed adjuvant chemotherapy within 48 months were recruited across Canada from February to July 2023. The combined exercise group engaged in unsupervised aerobic exercise (30 minutes, thrice per week), supervised group-based resistance training (30 minutes, twice per week via Zoom [Zoom Communications, Inc]), and one recorded class weekly, supplemented with 4 biweekly behavioral counseling sessions. The active control group participated in low-intensity balance and flexibility classes (30 minutes, twice per week live, once per week recorded). Feasibility was assessed via enrollment, adherence, attrition, measurement completion, adverse events, and participant satisfaction; cognitive function, using the National Institutes of Health Toolbox Cognition Battery Remote Administration (V2) at baseline and post intervention. RESULTS Twenty-one participants (mean age 51.6, SD 7.2 years; 11.8, SD 12.9 months since treatment) were randomized to the combined exercise (n=10) or the active control (n=11) group. Final analyses included 18 participants (mean age 51.9, SD 7.4 years; mean months since treatment 12.6, SD 13.5) with 9 participants in each group (51.2% enrollment rate; 14.3% attrition; no adverse events). Measurement completion rates were 85.7% and participants reported high satisfaction with the intervention, indicating minimal burden. Adherence rates for exercise classes were 70.8% in both groups. Adherence to behavioral counseling sessions was 77.5% (combined exercise group only). There were no significant differences in objectively measured cognitive function, but small-to-medium effect size improvements were observed in objectively measured episodic memory (mean difference 5.33, 95% CI –12.5 to 23.2; ηp²=0.03), working memory (mean difference 8.17, 95% CI –4.2 to 20.6; ηp²=0.12), executive function updating (mean difference –394.35, 95% CI –1035.67 to 246.96 ms; ηp²=0.07), and immediate memory and verbal learning (mean difference +3.22, 95% CI –2.0 to 8.5; ηp²=0.12), trending toward the multicomponent exercise group vs the active control group. In contrast, small-to-medium effect size improvements were observed in the Oral Reading Recognition Test (mean difference –9.65, 95% CI –22.9 to 3.5; P=.14; ηp²=0.14) and Picture Vocabulary Test (mean difference –2.48, 95% CI –5.2 to 0.3, P=.07; ηp²=0.20), trending toward the active control group. CONCLUSIONS A remotely delivered combined exercise intervention is feasible and may improve CRCI in patients with BC. Larger randomized controlled trials are warranted to confirm its efficacy in enhancing cognitive function and quality of life in this population. CLINICALTRIAL ClinicalTrials.gov NCT05704855; https://clinicaltrials.gov/study/NCT05704855