[HTML][HTML] Breast cancer resistance to chemotherapy: When should we suspect it and how can we prevent it?

M Faruk - Annals of Medicine and Surgery, 2021 - Elsevier
Chemotherapy is an essential treatment for breast cancer, inducing cancer cell death.
However, chemoresistance is a problem that limits the effectiveness of chemotherapy. Many …

Novel machine learning approach for the prediction of hernia recurrence, surgical complication, and 30-day readmission after abdominal wall reconstruction

AM Hassan, SC Lu, M Asaad, J Liu… - Journal of the …, 2022 - journals.lww.com
BACKGROUND: Despite advancements in abdominal wall reconstruction (AWR)
techniques, hernia recurrences (HRs), surgical site occurrences (SSOs), and unplanned …

Machine learning in medicine: a practical introduction to techniques for data pre-processing, hyperparameter tuning, and model comparison

A Pfob, SC Lu, C Sidey-Gibbons - BMC medical research methodology, 2022 - Springer
Background There is growing enthusiasm for the application of machine learning (ML) and
artificial intelligence (AI) techniques to clinical research and practice. However, instructions …

Intelligent vacuum-assisted biopsy to identify breast cancer patients with pathologic complete response (ypT0 and ypN0) after neoadjuvant systemic treatment for …

A Pfob, C Sidey-Gibbons, G Rauch… - Journal of Clinical …, 2022 - ascopubs.org
PURPOSE Neoadjuvant systemic treatment (NST) elicits a pathologic complete response in
40%-70% of women with breast cancer. These patients may not need surgery as all local …

MRI-based radiomics in breast cancer: feature robustness with respect to inter-observer segmentation variability

RWY Granzier, NMH Verbakel, A Ibrahim… - scientific reports, 2020 - nature.com
Radiomics is an emerging field using the extraction of quantitative features from medical
images for tissue characterization. While MRI-based radiomics is still at an early stage, it …

Towards patient-centered decision-making in breast cancer surgery: machine learning to predict individual patient-reported outcomes at 1-year follow-up

A Pfob, BJ Mehrara, JA Nelson, EG Wilkins… - Annals of …, 2023 - journals.lww.com
Objective: We developed, tested, and validated machine learning algorithms to predict
individual patient-reported outcomes at 1-year follow-up to facilitate individualized, patient …

Quantitative digital histopathology and machine learning to predict pathological complete response to chemotherapy in breast cancer patients using pre-treatment …

K Saednia, A Lagree, MA Alera, L Fleshner, A Shiner… - Scientific Reports, 2022 - nature.com
Complete pathological response (pCR) to neoadjuvant chemotherapy (NAC) is a prognostic
factor for breast cancer (BC) patients and is correlated with improved survival. However …

[HTML][HTML] Intelligent multi-modal shear wave elastography to reduce unnecessary biopsies in breast cancer diagnosis (INSPiRED 002): a retrospective, international …

A Pfob, C Sidey-Gibbons, RG Barr, V Duda… - European Journal of …, 2022 - Elsevier
Background Breast ultrasound identifies additional carcinomas not detected in
mammography but has a higher rate of false-positive findings. We evaluated whether use of …

Breast cancer surgery: new issues

F Magnoni, S Alessandrini, L Alberti, A Polizzi, A Rotili… - Current …, 2021 - mdpi.com
Since ancient times, breast cancer treatment has crucially relied on surgeons and clinicians
making great efforts to find increasingly conservative approaches to cure the tumor. In the …

[HTML][HTML] Machine learning to predict individual patient-reported outcomes at 2-year follow-up for women undergoing cancer-related mastectomy and breast …

A Pfob, BJ Mehrara, JA Nelson, EG Wilkins, AL Pusic… - The Breast, 2021 - Elsevier
Background Women undergoing cancer-related mastectomy and reconstruction are facing
multiple treatment choices where post-surgical satisfaction with breasts is a key outcome …