Chemotherapy

Opening the Black Box: Spatial Transcriptomics and the Relevance of Artificial Intelligence-Detected Prognostic Regions in High-Grade Serous Carcinoma.

Image-based deep learning models are used to extract new information from standard hematoxylin and e...

Machine-learning and mechanistic modeling of metastatic breast cancer after neoadjuvant treatment.

Clinical trials involving systemic neoadjuvant treatments in breast cancer aim to shrink tumors befo...

Deep learning-based multi-model prediction for disease-free survival status of patients with clear cell renal cell carcinoma after surgery: a multicenter cohort study.

BACKGROUND: Although separate analysis of individual factor can somewhat improve the prognostic perf...

Letter to the Editor Regarding Article "Prior to Initiation of Chemotherapy, Can We Predict Breast Tumor Response? Deep Learning Convolutional Neural Networks Approach Using a Breast MRI Tumor Dataset".

The cited article reports on a convolutional neural network trained to predict response to neoadjuva...

Determining individual suitability for neoadjuvant systemic therapy in breast cancer patients through deep learning.

BACKGROUND: The survival advantage of neoadjuvant systemic therapy (NST) for breast cancer patients ...

A machine learning approach for predicting textbook outcome after cytoreductive surgery and hyperthermic intraperitoneal chemotherapy.

INTRODUCTION: Peritoneal carcinomatosis is considered a late-stage manifestation of neoplastic disea...

Develop and Validate a Nomogram Combining Contrast-Enhanced Spectral Mammography Deep Learning with Clinical-Pathological Features to Predict Neoadjuvant Chemotherapy Response in Patients with ER-Positive/HER2-Negative Breast Cancer.

RATIONALE AND OBJECTIVES: To develop and validate a nomogram that combines contrast-enhanced spectra...

Machine Learning-Based Prediction of Pathological Responses and Prognosis After Neoadjuvant Chemotherapy for Non-Small-Cell Lung Cancer: A Retrospective Study.

BACKGROUND: Neoadjuvant chemotherapy has variable efficacy in patients with non-small-cell lung canc...

Is Risk-Stratifying Patients with Colorectal Cancer Using a Deep Learning-Based Prognostic Biomarker Cost-Effective?

OBJECTIVES: Accurate risk stratification of patients with stage II and III colorectal cancer (CRC) p...

Social robotics as an adjuvant during the hospitalization process in pediatric oncology patients.

OBJECTIVE: To describe the experience of implementing social robotics as an adjuvant during the hosp...

Noninvasive Artificial Intelligence System for Early Predicting Residual Cancer Burden During Neoadjuvant Chemotherapy in Breast Cancer.

OBJECTIVE: To develop an artificial intelligence (AI) system for the early prediction of residual ca...

Development of risk-score model in patients with negative surgical margin after robot-assisted radical prostatectomy.

A total of 739 patients underwent RARP as initial treatment for PCa from November 2011 to October 20...

Comparing survival of older ovarian cancer patients treated with neoadjuvant chemotherapy versus primary cytoreductive surgery: Reducing bias through machine learning.

OBJECTIVE: To develop and evaluate a multidimensional comorbidity index (MCI) that identifies ovaria...

Non-invasive prediction for pathologic complete response to neoadjuvant chemoimmunotherapy in lung cancer using CT-based deep learning: a multicenter study.

Neoadjuvant chemoimmunotherapy has revolutionized the therapeutic strategy for non-small cell lung c...

Empowering Graph Neural Networks with Block-Based Dual Adaptive Deep Adjustment for Drug Resistance-Related NcRNA Discovery.

Drug resistance to chemotherapeutic agents remains a formidable challenge in cancer treatment, signi...

Image-based artificial intelligence for the prediction of pathological complete response to neoadjuvant chemoradiotherapy in patients with rectal cancer: a systematic review and meta-analysis.

OBJECTIVE: Artificial intelligence (AI) holds enormous potential for noninvasively identifying patie...

Deep-learning-based radiomics of intratumoral and peritumoral MRI images to predict the pathological features of adjuvant radiotherapy in early-stage cervical squamous cell carcinoma.

BACKGROUND: Surgery combined with radiotherapy substantially escalates the likelihood of encounterin...

Artificial intelligence measured 3D body composition to predict pathological response in rectal cancer patients.

BACKGROUND: The treatment of locally advanced rectal cancer (LARC) is moving towards total neoadjuva...

Fully semantic segmentation for rectal cancer based on post-nCRT MRl modality and deep learning framework.

PURPOSE: Rectal tumor segmentation on post neoadjuvant chemoradiotherapy (nCRT) magnetic resonance i...

Simplex Lattice Design and Machine Learning Methods for the Optimization of Novel Microemulsion Systems to Enhance p-Coumaric Acid Oral Bioavailability: In Vitro and In Vivo Studies.

Novel p-coumaric acid microemulsion systems were developed to circumvent its absorption and bioavail...

Applications of Machine Learning on Alopecia Areata: A Systematic Review.