ABSTRACT

AIMS: With improved diagnosis and treatments, a greater percentage of breast cancer patients are achieving long-term survival. Consequently, long-term cardiotoxicity secondary to chemotherapy has become more prevalent, warranting improved cardiac surveillance. We evaluated changes in left atrial (LA) strain in breast cancer patients immediately post anthracycline (AC) therapy to assess its utility as a marker of diastolic dysfunction.

METHODS: This was a prospective cohort study of 128 consecutive human epidermal growth factor receptor 2 (HER2)-negative breast cancer patients who underwent transthoracic echocardiography prior to and immediately post AC treatment. Traditional left ventricular (LV) systolic and diastolic parameters and LA volumes were evaluated; additionally, LV global longitudinal strain (LV GLS) and LA phasic strain were measured.

RESULTS: All patients had normal LV ejection fraction (>53%) post AC, though LV GLS was significantly reduced. Peak E and é velocities were reduced post AC, with no change in LA volumes. LA reservoir strain (LASRES 34.8% vs 31.5%, p<0.001)CD 17.2% vs 14.4%, p<0.001)RES from baseline) (32%) compared to alteration in systolic function (≥15% reduction in LV GLS) (23%).

CONCLUSIONS: LA strain is a promising marker of early diastolic dysfunction. We demonstrate its potential utility in surveillance of breast cancer patients treated with AC.

PMID:37806911 | DOI:10.1016/j.hlc.2023.06.864

17:11

PubMed articles on: Cancer & VTE/PE

Computer image analysis with artificial intelligence: a practical introduction to convolutional neural networks for medical professionals

Postgrad Med J. 2023 Oct 4:qgad095. doi: 10.1093/postmj/qgad095. Online ahead of print.

ABSTRACT

Artificial intelligence tools, particularly convolutional neural networks (CNNs), are transforming healthcare by enhancing predictive, diagnostic, and decision-making capabilities. This review provides an accessible and practical explanation of CNNs for clinicians and highlights their relevance in medical image analysis. CNNs have shown themselves to be exceptionally useful in computer vision, a field that enables machines to 'see' and interpret visual data. Understanding how these models work can help clinicians leverage their full potential, especially as artificial intelligence continues to evolve and integrate into healthcare. CNNs have already demonstrated their efficacy in diverse medical fields, including radiology, histopathology, and medical photography. In radiology, CNNs have been used to automate the assessment of conditions such as pneumonia, pulmonary embolism, and rectal cancer. In histopathology, CNNs have been used to assess and classify colorectal polyps, gastric epithelial tumours, as well as assist in the assessment of multiple malignancies. In medical photography, CNNs have been used to assess retinal diseases and skin conditions, and to detect gastric and colorectal polyps during endoscopic procedures. In surgical laparoscopy, they may provide intraoperative assistance to surgeons, helping interpret surgical anatomy and demonstrate safe dissection zones. The integration of CNNs into medical image analysis promises to enhance diagnostic accuracy, streamline workflow efficiency, and expand access to expert-level image analysis, contributing to the ultimate goal of delivering further improvements in patient and healthcare outcomes.

PMID:37794609 | DOI:10.1093/postmj/qgad095

17:11

PubMed articles on: Cancer & VTE/PE

Development and validation of a new risk assessment model for immunomodulatory drug-associated venous thrombosis among Chinese patients with multiple myeloma

Thromb J. 2023 Oct 4;21(1):105. doi: 10.1186/s12959-023-00534-y.