Chemotherapy

Machine Learning-Driven Optimization of Therapeutic Substance Composition for High-Hardness, Fast-Dissolving Microneedles for Androgenetic Alopecia Treatment.

Treating androgenetic alopecia (AGA) with platelet-rich plasma (PRP) holds great promise; however, e...

Deep learning-based radiomics does not improve residual cancer burden prediction post-chemotherapy in LIMA breast MRI trial.

OBJECTIVES: This study aimed to evaluate the potential additional value of deep radiomics for assess...

Predicting Chemotherapy-Induced Peripheral Neuropathy Using Transformer-Based Multimodal Deep Learning.

Chemotherapy-induced peripheral neuropathy (CIPN) is a common and debilitating adverse effect of ca...

Rethinking cancer of unknown primary: from diagnostic challenge to targeted treatment.

Cancer of unknown primary (CUP) is a metastatic malignancy for which a primary site of origin cannot...

Machine learning and SHAP value interpretation for predicting the response to neoadjuvant chemotherapy and long-term clinical outcomes in Chinese female breast cancer.

BACKGROUND: Most models of neoadjuvant chemotherapy (NACT) for breast cancer (BC) suffer from insuff...

Impact of large language models and vision deep learning models in predicting neoadjuvant rectal score for rectal cancer treated with neoadjuvant chemoradiation.

This study aims to explore Deep Learning methods, namely Large Language Models (LLMs) and Computer V...

Integrative network pharmacology and multi-omics reveal anisodamine hydrobromide's multi-target mechanisms in sepsis.

Sepsis, marked by hyperinflammation and subsequent immunosuppression, lacks effective phase-specific...

Machine learning-assisted exploration of multidrug-drug administration regimens for organoid arrays.

Combination therapies enhance the therapeutic effect of cancer treatment; however, identifying effec...

Radiation enteritis associated with temporal sequencing of total neoadjuvant therapy in locally advanced rectal cancer: a preliminary study.

BACKGROUND: This study aimed to develop and validate a multi-temporal magnetic resonance imaging (MR...

Economic evaluation of a robotic chemotherapy compounding system and its service expansion to network hospital in Thailand.

BACKGROUND: Robotic systems for chemotherapy preparation offer improved accuracy and staff safety bu...

Refined prognostication of pathological complete response in breast cancer using radiomic features and optimized InceptionV3 with DCE-MRI.

BACKGROUND: Neoadjuvant therapy plays a pivotal role in breast cancer treatment, particularly for pa...

Plasmonic Fiber Optic Sensing Platform for Point-of-Care Pharmacokinetic Monitoring of Platinum Chemotherapeutics: Toward Ultratrace Multi-omics Precision Chemotherapy Management.

Precision chemotherapy management requires efficient and ultrasensitive dynamic monitoring of drug p...

Gut microbiota and SCFAs improve the treatment efficacy of chemotherapy and immunotherapy in NSCLC.

The role of gut dysbiosis in shaping immunotherapy responses is well-recognized, yet its effect on t...

Letter to the editor: multichannel deep learning prediction of major pathological response after neoadjuvant immunochemotherapy in lung cancer: a multicenter diagnostic study.

Artificial Intelligence and Trichoscopy: Potentials and Pitfalls in Alopecia Diagnosis.

Machine-learning driven strategies for adapting immunotherapy in metastatic NSCLC.

Immune checkpoint inhibitors (ICIs), either as monotherapy (ICI-Mono) or combined with chemotherapy ...

CT-based intratumoral and peritumoral radiomics to predict the treatment response to hepatic arterial infusion chemotherapy plus lenvatinib and PD-1 in high-risk hepatocellular carcinoma cases: a multi-center study.

BACKGROUND: Noninvasive and precise tools for treatment response estimation in patients with high-ri...

T cell engagers: expanding horizons in oncology and beyond.

BACKGROUND/INTRODUCTION: T cell engagers (TCEs) are engineered immunotherapeutic molecules designed ...

Sub-regional radiomics combining multichannel 2-dimensional or 3-dimensional deep learning for predicting neoadjuvant chemo-immunotherapy response in esophageal squamous cell carcinoma: a multicenter study.

This study aimed to develop and compare fusion models combining sub-regional radiomics with multicha...

Predicting Pathological Complete Response Following Neoadjuvant Therapy in Patients With Breast Cancer: Development of Machine Learning-Based Prediction Models in a Retrospective Study.

BACKGROUND: Breast cancer is the most prevalent form of cancer worldwide, with 2.3 million new diagn...

Exploring personalized neoadjuvant therapy selection strategies in breast cancer: an explainable multi-modal response model.

BACKGROUND: Neoadjuvant therapy (NAT) regimens for breast cancer are generally determined according ...