Journal Articles

Permanent URI for this collectionhttps://mro.massey.ac.nz/handle/10179/7915

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    Skeletal muscle mass, strength, and physical performance gains are similar between healthy postmenopausal women and postmenopausal breast cancer survivors after 12 weeks of resistance exercise training.
    (Springer Nature, 2024-11-23) Artigas-Arias M; Alegría-Molina A; Vidal-Seguel N; Muñoz-Cofre R; Carranza-Leiva J; Sepúlveda-Lara A; Vitzel KF; Huard N; Sapunar J; Salazar LA; Curi R; Marzuca-Nassr GN
    Purpose Resistance exercise training (RET) effectively increases skeletal muscle mass and strength in healthy postmenopausal women. However, its effects on these parameters in postmenopausal breast cancer survivors are controversial or limited. Therefore, the aim of this study was to compare the effects of a 12-week progressive whole-body RET program on skeletal muscle mass, strength, and physical performance in healthy postmenopausal women versus postmenopausal women who survived breast cancer. Methods Thirteen healthy postmenopausal women (HEA, 54 ± 3 years, BMI 26.6 ± 2.7 kg·m2, n = 13) and eleven postmenopausal breast cancer survivors (BCS, 52 ± 5 years, BMI 26.8 ± 2.1 kg·m2, n = 11) participated in the study. Before and after the RET program, evaluations were performed on quadriceps muscle thickness, one-repetition maximum strength (1RM) for various exercises, grip strength, and physical performance. Results Both groups showed significant improvements in quadriceps muscle thickness (time effect, P < 0.001); 1RM strength for leg extension, leg press, chest press, horizontal row, and elbow extension (time effect, all P < 0.001); as well as handgrip strength (time effect, P = 0.035) and physical performance (time effect, all P < 0.001) after the 12-week RET program. There were no significant differences between the groups in response to RET for any of the outcomes measured. Conclusion Twelve weeks of RET significantly increases skeletal muscle mass, strength, and physical performance in postmenopausal women. No differences were observed between healthy postmenopausal women and postmenopausal breast cancer survivors. These findings point out that this study’s RET promotes skeletal muscle mass, strength, and performance gains regardless of breast cancer. Pre-Print Platform Research Square: https://doi.org/10.21203/rs.3.rs-4145715/v1; https://www.researchsquare.com/article/rs-4145715/v1 Clinical trial registration: NCT05690295.
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    Potential rapid intraoperative cancer diagnosis using dynamic full-field optical coherence tomography and deep learning: A prospective cohort study in breast cancer patients
    (Elsevier B V on behalf of the Science China Press, 2024-06-15) Zhang S; Yang B; Yang H; Zhao J; Zhang Y; Gao Y; Monteiro O; Zhang K; Liu B; Wang S
    An intraoperative diagnosis is critical for precise cancer surgery. However, traditional intraoperative assessments based on hematoxylin and eosin (H&E) histology, such as frozen section, are time-, resource-, and labor-intensive, and involve specimen-consuming concerns. Here, we report a near-real-time automated cancer diagnosis workflow for breast cancer that combines dynamic full-field optical coherence tomography (D-FFOCT), a label-free optical imaging method, and deep learning for bedside tumor diagnosis during surgery. To classify the benign and malignant breast tissues, we conducted a prospective cohort trial. In the modeling group (n = 182), D-FFOCT images were captured from April 26 to June 20, 2018, encompassing 48 benign lesions, 114 invasive ductal carcinoma (IDC), 10 invasive lobular carcinoma, 4 ductal carcinoma in situ (DCIS), and 6 rare tumors. Deep learning model was built up and fine-tuned in 10,357 D-FFOCT patches. Subsequently, from June 22 to August 17, 2018, independent tests (n = 42) were conducted on 10 benign lesions, 29 IDC, 1 DCIS, and 2 rare tumors. The model yielded excellent performance, with an accuracy of 97.62%, sensitivity of 96.88% and specificity of 100%; only one IDC was misclassified. Meanwhile, the acquisition of the D-FFOCT images was non-destructive and did not require any tissue preparation or staining procedures. In the simulated intraoperative margin evaluation procedure, the time required for our novel workflow (approximately 3 min) was significantly shorter than that required for traditional procedures (approximately 30 min). These findings indicate that the combination of D-FFOCT and deep learning algorithms can streamline intraoperative cancer diagnosis independently of traditional pathology laboratory procedures.