Chemotherapy

Response prediction for neoadjuvant treatment in locally advanced rectal cancer patients-improvement in decision-making: A systematic review.

BACKGROUND: Predicting pathological complete response (pCR) from pre or post-treatment features could be significant in improving the process of making clinical decisions and providing a more personalized treatment approach for better treatment outcomes. However, the lack of external validation of predictive models, missing in several published articles, is a major issue that can potentially limit...

Prediction of 12-month recurrence of pancreatic cancer using machine learning and prognostic factors.

BACKGROUND AND AIM: Pancreatic cancer is lethal and prevalent among other cancer types. The recurrence of this tumor is high, especially in patients who did not receive adjuvant therapies. Early prediction of PC recurrence has a significant role in enhancing patients' prognosis and survival. So far, machine learning techniques have given us insight into favorable performance efficiency in various ...

Early and noninvasive prediction of response to neoadjuvant therapy for breast cancer via longitudinal ultrasound and MR deep learning: A multicentre study.

RATIONALE AND OBJECTIVES: The early prediction of response to neoadjuvant chemotherapy (NAC) will aid in the development of personalized treatments fo...

SNPs and blood inflammatory marker featured machine learning for predicting the efficacy of fluorouracil-based chemotherapy in colorectal cancer.

Fluorouracil-based chemotherapy responses in colorectal cancer (CRC) patients vary widely, highlighting the role of pharmacogenomics in developing bet...

Integration of Machine Learning and Experimental Validation to Identify Anoikis-Related Prognostic Signature for Predicting the Breast Cancer Tumor Microenvironment and Treatment Response.

Anoikis-related genes (ANRGs) are crucial in the invasion and metastasis of breast cancer (BC). The underlying role of ANRGs in the prognosis of brea...

The established of a machine learning model for predicting the efficacy of adjuvant interferon alpha1b in patients with advanced melanoma.

BACKGROUND: Interferon-alpha1b (IFN-α1b) has shown remarkable therapeutic potential as adjuvant therapy for melanoma. This study aimed to develop five...

Exploring the cytotoxic and antioxidant properties of lanthanide-doped ZnO nanoparticles: a study with machine learning interpretation.

BACKGROUND: Lanthanide-based nanomaterials offer a promising alternative for cancer therapy because of their selectivity and effectiveness, which can ...

An explainable longitudinal multi-modal fusion model for predicting neoadjuvant therapy response in women with breast cancer.

Multi-modal image analysis using deep learning (DL) lays the foundation for neoadjuvant treatment (NAT) response monitoring. However, existing methods...

Utilizing machine learning and molecular dynamics for enhanced drug delivery in nanoparticle systems.

Materials data science and machine learning (ML) are pivotal in advancing cancer treatment strategies beyond traditional methods like chemotherapy. Na...

From prediction to practice: mitigating bias and data shift in machine-learning models for chemotherapy-induced organ dysfunction across unseen cancers.

OBJECTIVES: Routine monitoring of renal and hepatic function during chemotherapy ensures that treatment-related organ damage has not occurred and clea...

Deep learning-based automatic image classification of oral cancer cells acquiring chemoresistance in vitro.

Cell shape reflects the spatial configuration resulting from the equilibrium of cellular and environmental signals and is considered a highly relevant...

The role of aspirin in preventing gastrointestinal cancers.

Cancer remains an increasing global health issue and is projected to cause 50% of all global deaths by 2050. Gastrointestinal (GI) tract cancers curre...

A Machine Learning-Optimized System for Pulsatile, Photo- and Chemotherapeutic Treatment Using Near-Infrared Responsive MoS-Based Microparticles in a Breast Cancer Model.

Multimodal cancer therapies are often required for progressive cancers due to the high persistence and mortality of the disease and the negative syste...

Clinical evaluation of accelerated diffusion-weighted imaging of rectal cancer using a denoising neural network.

BACKGROUND: To evaluate the effectiveness of a deep learning denoising approach to accelerate diffusion-weighted imaging (DWI) and thus improve diagno...

Predicting Breast Cancer Relapse from Histopathological Images with Ensemble Machine Learning Models.

Relapse and metastasis occur in 30-40% of breast cancer patients, even after targeted treatments like trastuzumab for HER2-positive breast cancer. Acc...

Bloodstream Infections in Childhood Acute Myeloid Leukemia and Machine Learning Models: A Single-institutional Analysis.

Childhood acute myeloid leukemia (AML) requires intensive chemotherapy, which may result in life-threatening bloodstream infections (BSIs). This study...

TopoTxR: A topology-guided deep convolutional network for breast parenchyma learning on DCE-MRIs.

Characterization of breast parenchyma in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a challenging task owing to the complexity ...

A Multicenter Cohort Study on Ultrasound-based Deep Learning Nomogram for Predicting Post-Neoadjuvant Chemotherapy Axillary Lymph Node Status in Breast Cancer Patients.

RATIONALE AND OBJECTIVES: The aim of this study was to evaluate the capability of an ultrasound (US)-based deep learning (DL) nomogram for predicting ...

A Study of Serum Ferritin and Thyroid-Stimulating Hormone Levels in Male Patients with Androgenetic Alopecia.

BACKGROUND: Androgenetic alopecia (AGA) is a multifactorial disorder expressed by several genes and various environmental factors apart from nutrition...

Prediction of pathological complete response to chemotherapy for breast cancer using deep neural network with uncertainty quantification.

BACKGROUND: The I-SPY 2 trial is a national-wide, multi-institutional clinical trial designed to evaluate multiple new therapeutic drugs for high-risk...