AI Medical Compendium Journal:
Breast (Edinburgh, Scotland)

Showing 1 to 10 of 23 articles

Optimization of guidelines for Risk Of Recurrence/Prosigna testing using a machine learning model: a Swedish multicenter study.

Breast (Edinburgh, Scotland)
PURPOSE: Gene expression profiles are used for decision making in the adjuvant setting in hormone receptor-positive, HER2-negative (HR+/HER2-) breast cancer. While algorithms to optimize testing exist for RS/Oncotype Dx, no such efforts have focused ...

Computational pathology for breast cancer: Where do we stand for prognostic applications?

Breast (Edinburgh, Scotland)
The very early days of artificial intelligence (AI) in healthcare are behind us. AI is now spreading in the healthcare sector and is gradually being implemented in routine clinical practice. Driven by the increasing digitization of microscope slides,...

Personalized treatment strategies for breast adenoid cystic carcinoma: A machine learning approach.

Breast (Edinburgh, Scotland)
BACKGROUND: Breast adenoid cystic carcinoma (BACC) is a rare subtype of breast cancer that accounts for less than 0.1 % of all cases. This study was designed to assess the efficacy of various treatment approaches for BACC and to create the first web-...

Novel models based on machine learning to predict the prognosis of metaplastic breast cancer.

Breast (Edinburgh, Scotland)
BACKGROUND: Metaplastic breast cancer (MBC) is a rare and highly aggressive histological subtype of breast cancer. There remains a significant lack of precise predictive models available for use in clinical practice.

AI-based strategies in breast mass ≤ 2 cm classification with mammography and tomosynthesis.

Breast (Edinburgh, Scotland)
PURPOSE: To evaluate the diagnosis performance of digital mammography (DM) and digital breast tomosynthesis (DBT), DM combined DBT with AI-based strategies for breast mass ≤ 2 cm.

Non-invasive prediction of axillary lymph node dissection exemption in breast cancer patients post-neoadjuvant therapy: A radiomics and deep learning analysis on longitudinal DCE-MRI data.

Breast (Edinburgh, Scotland)
PURPOSE: In breast cancer (BC) patients with clinical axillary lymph node metastasis (cN+) undergoing neoadjuvant therapy (NAT), precise axillary lymph node (ALN) assessment dictates therapeutic strategy. There is a critical demand for a precise meth...

Women's views on using artificial intelligence in breast cancer screening: A review and qualitative study to guide breast screening services.

Breast (Edinburgh, Scotland)
As breast screening services move towards use of healthcare AI (HCAI) for screen reading, research on public views of HCAI can inform more person-centered implementation. We synthesise reviews of public views of HCAI in general, and review primary st...

Assessment of deep learning-based auto-contouring on interobserver consistency in target volume and organs-at-risk delineation for breast cancer: Implications for RTQA program in a multi-institutional study.

Breast (Edinburgh, Scotland)
PURPOSE: To quantify interobserver variation (IOV) in target volume and organs-at-risk (OAR) contouring across 31 institutions in breast cancer cases and to explore the clinical utility of deep learning (DL)-based auto-contouring in reducing potentia...

Deep learning with biopsy whole slide images for pretreatment prediction of pathological complete response to neoadjuvant chemotherapy in breast cancer:A multicenter study.

Breast (Edinburgh, Scotland)
INTRODUCTION: Predicting pathological complete response (pCR) for patients receiving neoadjuvant chemotherapy (NAC) is crucial in establishing individualized treatment. Whole-slide images (WSIs) of tumor tissues reflect the histopathologic informatio...

The usefulness of CanAssist breast in the assessment of recurrence risk in patients of ethnic Indian origin.

Breast (Edinburgh, Scotland)
Accurate recurrence risk assessment in hormone receptor positive, HER2/neu negative breast cancer is critical to plan precise therapy. CanAssist Breast (CAB) assesses recurrence risk based on tumor biology using artificial intelligence-based approach...