AIMC Topic: Neoadjuvant Therapy

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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 ...

Evaluation of Neoadjuvant Chemoradiotherapy Response in Rectal Cancer Using MR Images and Deep Learning Neural Networks.

Current medical imaging
INTRODUCTION: The aim of the study was to develop deep-learning neural networks to guide treatment decisions and for the accurate evaluation of tumor response to neoadjuvant chemoradiotherapy (nCRT) in rectal cancer using magnetic resonance (MR) imag...

ApoA-I and ApoB levels, and ApoB-to-ApoA-I ratio as candidate pre-treatment biomarkers of pathomorphological response to neoadjuvant therapy in gastric and esophago-gastric junction adenocarcinoma.

Polski przeglad chirurgiczny
<b><br>Introduction:</b> Neoadjuvant chemotherapy (NAC) is a part of the current standard of care in a locally advanced gastric adenocarcinoma (GA) and esophagogastric junction adenocarcinoma (EGJA), but only patients with good path...

Histology-Based Prediction of Therapy Response to Neoadjuvant Chemotherapy for Esophageal and Esophagogastric Junction Adenocarcinomas Using Deep Learning.

JCO clinical cancer informatics
PURPOSE: Quantifying treatment response to gastroesophageal junction (GEJ) adenocarcinomas is crucial to provide an optimal therapeutic strategy. Routinely taken tissue samples provide an opportunity to enhance existing positron emission tomography-c...

Predicting pathological complete response to neoadjuvant systemic therapy for triple-negative breast cancers using deep learning on multiparametric MRIs.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
We trained and validated a deep learning model that can predict the treatment response to neoadjuvant systemic therapy (NAST) for patients with triple negative breast cancer (TNBC). Dynamic contrast enhanced (DCE) MRI and diffusion-weighted imaging (...

[A Case of Locally Advanced Sigmoid Colon Cancer Invading the Urinary Bladder Treated with Neoadjuvant Chemotherapy and Robot Assisted Urinary Bladder-Conserving Surgery].

Gan to kagaku ryoho. Cancer & chemotherapy
We report a case of 72s male with locally advanced sigmoid colon cancer. Colonoscopy revealed an advanced sigmoid colon cancer(AV 15 cm, type 2, semi-peripheral, deeper than T3). He was diagnosed as cT4bN2M0, cStage Ⅲc(Japanese Classification of Colo...

[Survival of patients with pancreatic ductal adenocarcinoma].

Revista de gastroenterologia del Peru : organo oficial de la Sociedad de Gastroenterologia del Peru
OBJECTIVE: The objective of this study is to analyze the epidemiological presentation and survival of patients with pancreatic ductal adenocarcinoma according to their clinical stage and the type of intervention performed, in a cohort of patients tre...