Adjuvant treatment for breast cancer

Flatley, M.

doi:10.1016/j.mpsur.2019.02.005

Cited by 2 publications

(2 citation statements)

References 11 publications

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“…It may consist of local treatment (radiotherapy) and systemic treatments (chemotherapy, endocrine and biological therapies). Recent advances in therapeutic patterns have developed more precise delivery of adjuvant treatment with an improvement in survival rates by reducing the risk of local and distant recurrence [ 4 ]. Despite the effectiveness of contemporary anticancer therapies, there are significant potential adverse effects.…”

Section: Introductionmentioning

confidence: 99%

Hypercoagulability State Combined with Post-Treatment Hypofibrinolysis in Invasive Breast Cancer: A Seven-Year Follow-Up Evaluating Disease-Free and Overall Survival

Wrzeszcz

Rhone²,

Kwiatkowska

et al. 2023

Life

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(1) Background: Cancer treatment, including chemotherapy, endocrine therapy, targeted therapy and radiotherapy, has been identified as an important independent risk factor for venous thromboembolism in cancer patients. The aim of the study was to evaluate the effect of adjuvant therapy on the coagulation and fibrinolysis components in invasive breast cancer. (2) Methods: Tissue factor pathway inhibitor (TFPI), tissue factor (TF), tissue plasminogen activator (t-PA), plasminogen activator inhibitor-1 (PAI-1) antigen (concentration) and TFPI and TF activities were examined in the blood samples of 60 breast cancer patients treated by adjuvant chemotherapy, endocrine therapy, radiotherapy and immunotherapy. Blood samples were taken 24 h before primary surgery and 8 months after tumour removal surgery. (3) Results: Adjuvant therapy administrated to breast cancer patients significantly increased the concentration of plasma TF, the PAI-1 antigen and also the activity of TFPI and TF, but significantly decreased the level of the t-PA antigen. Combined chemotherapy and endocrine therapy, but not monotherapy, has an important effect on haemostatic biomarker levels. (4) Conclusions: Breast cancer patients receiving adjuvant therapy have an elevated risk of developing a hypercoagulability and hypofibrinolysis state leading to venous thromboembolism.

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Section: Introductionmentioning

confidence: 99%

Hypercoagulability State Combined with Post-Treatment Hypofibrinolysis in Invasive Breast Cancer: A Seven-Year Follow-Up Evaluating Disease-Free and Overall Survival

Wrzeszcz

Rhone²,

Kwiatkowska

et al. 2023

Life

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“…Notably, a complete pathologic response with no residual tumor is indicative of an excellent prognosis in patients with HER2-positive and triple-negative tumors [19,20]. After surgical removal of the primary tumor, adjuvant therapy can be administered to eliminate any residual tumor in breast tissues or possibly in any other part of the body [21,22].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Prediction of Distant Recurrence in Invasive Breast Carcinoma Using Clinicopathological Data: A Cross-Institutional Study

Sukhadia

Muller

Workman

et al. 2023

Cancers

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Breast cancer is the most common type of cancer worldwide. Alarmingly, approximately 30% of breast cancer cases result in disease recurrence at distant organs after treatment. Distant recurrence is more common in some subtypes such as invasive breast carcinoma (IBC). While clinicians have utilized several clinicopathological measurements to predict distant recurrences in IBC, no studies have predicted distant recurrences by combining clinicopathological evaluations of IBC tumors pre- and post-therapy with machine learning (ML) models. The goal of our study was to determine whether classification-based ML techniques could predict distant recurrences in IBC patients using key clinicopathological measurements, including pathological staging of the tumor and surrounding lymph nodes assessed both pre- and post-neoadjuvant therapy, response to therapy via standard-of-care imaging, and binary status of adjuvant therapy administered to patients. We trained and tested four clinicopathological ML models using a dataset (144 and 17 patients for training and testing, respectively) from Duke University and validated the best-performing model using an external dataset (8 patients) from Dartmouth Hitchcock Medical Center. The random forest model performed better than the C-support vector classifier, multilayer perceptron, and logistic regression models, yielding AUC values of 1.0 in the testing set and 0.75 in the validation set (p < 0.002) across both institutions, thereby demonstrating the cross-institutional portability and validity of ML models in the field of clinical research in cancer. The top-ranking clinicopathological measurement impacting the prediction of distant recurrences in IBC were identified to be tumor response to neoadjuvant therapy as evaluated via SOC imaging and pathology, which included tumor as well as node staging.

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Adjuvant treatment for breast cancer

Cited by 2 publications

References 11 publications

Hypercoagulability State Combined with Post-Treatment Hypofibrinolysis in Invasive Breast Cancer: A Seven-Year Follow-Up Evaluating Disease-Free and Overall Survival

Hypercoagulability State Combined with Post-Treatment Hypofibrinolysis in Invasive Breast Cancer: A Seven-Year Follow-Up Evaluating Disease-Free and Overall Survival

Machine Learning-Based Prediction of Distant Recurrence in Invasive Breast Carcinoma Using Clinicopathological Data: A Cross-Institutional Study

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