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Neoadjuvant Therapy

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[Value of the deep learning automated quantification of tumor-stroma ratio in predicting efficacy and prognosis of neoadjuvant therapy for breast cancer based on residual cancer burden grading].

Zhonghua bing li xue za zhi = Chinese journal of pathology
To investigate the prognostic value of deep learning-based automated quantification of tumor-stroma ratio (TSR) in patients undergoing neoadjuvant therapy (NAT) for breast cancer. Specimens were collected from 209 breast cancer patients who receive...

Monitoring Over Time of Pathological Complete Response to Neoadjuvant Chemotherapy in Breast Cancer Patients Through an Ensemble Vision Transformers-Based Model.

Cancer medicine
BACKGROUND: Morphological and vascular characteristics of breast cancer can change during neoadjuvant chemotherapy (NAC). Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI)-acquired pre- and mid-treatment quantitatively capture informatio...

Development and validation of machine learning models for predicting HER2-zero and HER2-low breast cancers.

The British journal of radiology
OBJECTIVES: To develop and validate machine learning models for human epidermal growth factor receptor 2 (HER2)-zero and HER2-low using MRI features pre-neoadjuvant therapy (NAT).

Machine learning in predicting pathological complete response to neoadjuvant chemoradiotherapy in rectal cancer using MRI: a systematic review and meta-analysis.

The British journal of radiology
OBJECTIVES: To evaluate the performance of machine learning models in predicting pathological complete response (pCR) to neoadjuvant chemoradiotherapy (nCRT) in patients with rectal cancer using magnetic resonance imaging.

Machine Learning Links T-cell Function and Spatial Localization to Neoadjuvant Immunotherapy and Clinical Outcome in Pancreatic Cancer.

Cancer immunology research
Tumor molecular data sets are becoming increasingly complex, making it nearly impossible for humans alone to effectively analyze them. Here, we demonstrate the power of using machine learning (ML) to analyze a single-cell, spatial, and highly multipl...

Transforming breast cancer care: harnessing the power of artificial intelligence and imaging for predicting pathological complete response. a narrative review.

JPMA. The Journal of the Pakistan Medical Association
This narrative review explores the transformative potential of Artificial Intelligence (AI) and advanced imaging techniques in predicting Pathological Complete Response (pCR) in Breast Cancer (BC) patients undergoing Neo-Adjuvant Chemotherapy (NACT)....

Deep Learning of Multimodal Ultrasound: Stratifying the Response to Neoadjuvant Chemotherapy in Breast Cancer Before Treatment.

The oncologist
BACKGROUND: Not only should resistance to neoadjuvant chemotherapy (NAC) be considered in patients with breast cancer but also the possibility of achieving a pathologic complete response (PCR) after NAC. Our study aims to develop 2 multimodal ultraso...

Prediction of T Stage of Rectal Cancer After Neoadjuvant Therapy by Multi-Parameter Magnetic Resonance Radiomics Based on Machine Learning Algorithms.

Technology in cancer research & treatment
INTRODUCTION: Since the response of patients with rectal cancer (RC) to neoadjuvant therapy is highly variable, there is an urgent need to develop accurate methods to predict the post-treatment T (pT) stage. The purpose of this study was to evaluate ...