AIMC Topic: Mastectomy
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Explainable machine learning to compare the overall survival status between patients receiving mastectomy and breast conserving surgeries.
Scientific reports
Mar 28, 2025
The most prevalent malignancy among women is breast cancer; hence, treatment approaches are needed in consideration of tumor characteristics and disease stage but also patient preference. Two surgical options, Mastectomy and Breast Conserving Surgery...
Development of a deep learning-based model for guiding a dissection during robotic breast surgery.
Breast cancer research : BCR
Mar 10, 2025
BACKGROUND: Traditional surgical education is based on observation and assistance in surgical practice. Recently introduced deep learning (DL) techniques enable the recognition of the surgical view and automatic identification of surgical landmarks. ...
Artificial Intelligence in Breast Reconstruction: A Narrative Review.
Medicina (Kaunas, Lithuania)
Feb 28, 2025
Breast reconstruction following mastectomy or sectorectomy significantly impacts the quality of life and psychological well-being of breast cancer patients. Since its inception in the 1950s, artificial intelligence (AI) has gradually entered the medi...
Diagnosis of Benign and Malignant Newly Developed Nodules on the Surgical Side After Breast Cancer Surgery Based on Machine Learning.
The breast journal
Feb 17, 2025
To enhance the diagnostic accuracy of new nodules on the surgical side after breast cancer surgery using machine learning techniques and to explore the role of multifeature fusion. Data from 137 breast cancer postoperative patients with new nodules...
Exploring the Social Media Discussion of Breast Cancer Treatment Choices: Quantitative Natural Language Processing Study.
JMIR cancer
Jan 28, 2025
BACKGROUND: Early-stage breast cancer has the complex challenge of carrying a favorable prognosis with multiple treatment options, including breast-conserving surgery (BCS) or mastectomy. Social media is increasingly used as a source of information a...
Developing machine learning models for personalized treatment strategies in early breast cancer patients undergoing neoadjuvant systemic therapy based on SEER database.
Scientific reports
Sep 27, 2024
This study aimed to compare the long-term outcomes of breast-conserving surgery plus radiotherapy (BCS + RT) and mastectomy in early breast cancer (EBC) patients who received neoadjuvant systemic therapy (NST), and sought to construct and authenticat...
Machine Learning Radiomics-Based Prediction of Non-sentinel Lymph Node Metastasis in Chinese Breast Cancer Patients with 1-2 Positive Sentinel Lymph Nodes: A Multicenter Study.
Academic radiology
Mar 15, 2024
RATIONALE AND OBJECTIVES: This study aimed to construct a machine learning radiomics-based model using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) images to evaluate non-sentinel lymph node (NSLN) metastasis in Chinese breast cance...
Patient-reported outcomes of mesh in minimally invasive (laparoscopic/robot-assisted) immediate subpectoral prosthesis breast reconstruction: a retrospective study.
Breast cancer (Tokyo, Japan)
Feb 2, 2024
BACKGROUND: Although there is increasing interest in minimally invasive prosthesis breast reconstruction (PBR), whether meshes application in minimally invasive PBR can improve complications and cosmetic effects remains controversial. The author retr...
Comparative Effectiveness Analysis of Lumpectomy and Mastectomy for Elderly Female Breast Cancer Patients: A Deep Learning-based Big Data Analysis.
The Yale journal of biology and medicine
Sep 29, 2023
: To evaluate the comparative effectiveness of treatments, a randomized clinical trial remains the gold standard but can be challenged by a high cost, a limited sample size, an inability to fully reflect the real world, and feasibility concerns. The ...
Clinical evaluation of deep learning-based automatic clinical target volume segmentation: a single-institution multi-site tumor experience.
La Radiologia medica
Aug 19, 2023
PURPOSE: The large variability in tumor appearance and shape makes manual delineation of the clinical target volume (CTV) time-consuming, and the results depend on the oncologists' experience. Whereas deep learning techniques have allowed oncologists...
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