Cardiac assessment in Australian patients receiving (neo)adjuvant trastuzumab for HER2-positive early breast cancer: a population-based study

Comparison of Echocardiography and Multi-Planar Gated Acquisition Scans for Predicting Cancer-Treatment-Related Cardiovascular Dysfunction

BACKGROUND

Current guidelines recommend using sequential cardiac imaging to monitor for cancer treatment-related cardiac dysfunction (CTRCD) in patients undergoing potentially cardiotoxic chemotherapy. Multiple different imaging cardiac modalities are available and there are few prospective head-to-head comparative studies to help guide treatment.

OBJECTIVES

To perform an exploratory prospective cohort study of "real-world" CTRCD comparing multigated acquisition nuclear ventriculography (MUGA) at the referring cancer specialist's discretion with a novel echocardiographic strategy at an Australian tertiary hospital.

METHOD

Patients were recruited from haematology and oncology outpatient clinics if they were scheduled for treatment with anthracyclines and/or trastuzumab. Patients underwent simultaneous MUGA-based cardiac imaging (conventional strategy) at a frequency according to evidenced-based guidelines in addition to researcher-conducted echocardiographic imaging. The echocardiographic imaging was performed in all patients at time points recommended by international society guidelines. Outcomes included adherence to guideline recommendations, concordance between MUGA and echocardiographic left ventricular ejection fraction (LVEF) measurements, and detection of cardiac dysfunction (defined as >5% LVEF decrement from baseline by three-dimensional [3D]-LVEF). A secondary end point was accuracy of global longitudinal strain in predicting cardiac dysfunction.

RESULTS

In total, 35 patients were recruited, including 15 with breast cancer, 19 with haematological malignancy, and one with gastric cancer. MUGA and echocardiographic LVEF measurements correlated poorly with limits of agreement of 30% between 3D-LVEF and MUGA-LVEF and 37% for 3D-LVEF and MUGA-LVEF. Only one case (2.9%) of CTRCD was diagnosed by MUGA, compared with 12 (34.2%) cases by echocardiography. Four (4) patients had >10% decrement in 3D-LVEF that was not detected by MUGA. Global longitudinal strain at 2 months displayed significant ability to predict CTRCD (area under the curve, 0.75, 95% confidence interval, 0.55-0.94).

CONCLUSIONS

The MUGA correlates poorly with echocardiographic assessment with substantial discrepancy between MUGA and echocardiography in CTRCD diagnosis. Echocardiographic and MUGA imaging strategies should not be considered equivalent for imaging cancer patients, and a single imaging modality should ideally be used per patient to prevent misdiagnosis by inter-modality variation These findings should be considered hypothesis-generating and require confirmation with larger studies.

Artificial intelligence-enabled prediction of chemotherapy-induced cardiotoxicity from baseline electrocardiograms

Anthracyclines can cause cancer therapy-related cardiac dysfunction (CTRCD) that adversely affects prognosis. Despite guideline recommendations, only half of the patients undergo surveillance echocardiograms. An AI model detecting reduced left ventricular ejection fraction from 12-lead electrocardiograms (ECG) (AI-EF model) suggests ECG features reflect left ventricular pathophysiology. We hypothesized that AI could predict CTRCD from baseline ECG, leveraging the AI-EF model's insights, and developed the AI-CTRCD model using transfer learning on the AI-EF model. In 1011 anthracycline-treated patients, 8.7% experienced CTRCD. High AI-CTRCD scores indicated elevated CTRCD risk (hazard ratio (HR), 2.66; 95% CI 1.73-4.10; log-rank p < 0.001). This remained consistent after adjusting for risk factors (adjusted HR, 2.57; 95% CI 1.62-4.10; p < 0.001). AI-CTRCD score enhanced prediction beyond known factors (time-dependent AUC for 2 years: 0.78 with AI-CTRCD score vs. 0.74 without; p = 0.005). In conclusion, the AI model robustly stratified CTRCD risk from baseline ECG.

A Washing-Free and Easy-to-Operate Fluorescent Biosensor for Highly Efficient Detection of Breast Cancer-Derived Exosomes

Traditional detection methods for protein tumor markers in the early screening of breast cancer are restricted by complicated operation procedures and unstable reproducibility. As one of alternative emerging tumor markers, exosomes play an important role in diagnosing and treating cancers at the early stage due to traceability of their origins and great involvement in occurrence and development of cancers. Herein, a washing-free and efficient fluorescent biosensor has been proposed to realize simple and straightforward analysis of breast cancer cell-derived exosomes based on high affinity aptamers and G quadruplex-hemin (G4-hemin). The whole reaction process can be completed by several simple steps, which realizes washing-free and labor-saving. With simplified operation procedures and high repeatability, the linear detection range for this developed fluorescent biosensing strategy to breast cancer cell-derived exosomes is from 2.5 × 10⁵ to 1.00 × 10⁷ particles/ml, and the limit of detection is down to 0.54 × 10⁵ particles/ml.