Chemotherapy

The basis for future personalized therapy approaches - Machine learning-generated 1-year survival rate, metastatic status and therapy-dependent survival in pancreatic cancer patients.

BACKGROUND: Pancreatic adenocarcinoma (PDAC) remains one of the most lethal types of cancer, characterized by its unspecific symptoms, aggressive nature and late-stage diagnosis. PATIENTS AND METHODS: In this study, Machine Learning (ML) models were trained and applied on the largest international, monocentric database, comprising over 23 clinical variables from 1040 PDAC patients. In this study, ...

Integrative analysis of transcriptomic data reveals a predictive gene signature for chemoradiotherapy response in rectal cancer.

Locally advanced rectal cancer (LARC) is treated with neoadjuvant chemoradiotherapy (nCRT), but only a minority of patients achieve a pathological complete response (pCR). Predictive biomarkers of response could help guide treatment decisions, yet none have reached clinical practice. In this exploratory study, we integrated six publicly available transcriptomic datasets and applied machine learnin...

Modulating tumor mechanics and vascular barriers to enhance chemotherapy efficacy in pancreatic tumors and melanoma.

Pancreatic ductal adenocarcinoma (PDAC) is characterized by a dense stroma and dysfunctional vasculature, limiting drug delivery and immune infiltrati...

Interpretable fusion deep learning on super-resolution MRI for perineural invasion prediction in pancreatic ductal adenocarcinoma: a multicenter study.

PURPOSE: To develop an interpretable fusion deep learning model based on super-resolution (SR) MRI for predicting preoperative perineural invasion (PN...

Plant-derived angiotensin-converting enzyme regulatory peptides: Potential sources, mechanisms and applications.

Angiotensin-converting enzyme (ACE) is mainly categorized into ACE-1 and ACE-2, both of which play significant roles in human physiological balance an...

Preoperative Identification of Lymph Node Metastasis in Colorectal Cancer Using Noninvasive Circulating Tumor DNA Methylation Signatures: Results From a Prospective Study.

BACKGROUND: Lymph node metastasis is important for the management and surgical procedures of patients with colorectal cancer. Preoperative identificat...

The global epidemiology of acute myeloid leukaemia.

Acute myeloid leukaemia (AML) is an aggressive haematological malignancy with an incidence that increases with age and varies widely across regions ow...

Developing and Validating a Prediction Model for the Severe Pain-Fatigue-Sleep Disturbance Symptom Cluster in Patients with Lung Cancer Following Chemotherapy: A Machine Learning Analysis.

OBJECTIVES: Pain-fatigue-sleep disturbance symptom (PFS) cluster is the most common symptom cluster in patients with lung cancer following chemotherap...

Pharmacoepidemiologic characterization of cannabis use and symptomatology in rheumatology using natural language processing of electronic health record clinic notes.

Up to 70% of patients with autoimmune rheumatic diseases (ARDs), including rheumatoid arthritis, psoriatic arthritis, and systemic lupus erythematosus...

Application of machine learning to create a prediction model for the need of aggressive nutritional intervention during head & neck cancer radiotherapy.

BACKGROUND: Curative-intent radiotherapy (RT) or chemoradiotherapy (CRT) for head and neck squamous cell carcinoma (HNSCC) frequently leads to mucosit...

Raman imaging uncovers Ebracteolatain A's subtype-selective metabolic reprogramming in luminal versus triple-negative breast cancer cells.

Ebracteolatain A (EA), a potential anti-cancer agent, has demonstrated efficacy against breast cancer through protein kinase D1 inhibition. However, i...

Bot vs. doc-who is better at reading proximal humerus fracture x-rays?

BACKGROUND: Artificial intelligence is becoming increasingly utilized as a source of convenient, efficient, and cost-effective information. Considerin...

Effect of media age on cytotoxicity of drinking water treated by biological activated carbon filters and chlorine disinfection.

Biological activated carbon (BAC) filters are widely employed for controlling the formation of harmful disinfection byproducts (DBPs) prior to chlorin...

Integrating multiphase MRI surrogates to improve microvascular invasion detection and recurrence risk stratification in HCC.

BACKGROUND & AIMS: Microvascular invasion (MVI) is a key determinant of recurrence and poor outcomes in hepatocellular carcinoma (HCC), yet accurate p...

Drug Repurposing in Oncology: A Strategic Pathway to Unlocking New Therapeutic Potential.

The growing number of cancer cases and deaths highlights the urgent need for innovative treatment approaches. One technique that has lately been recog...

Combined dual-phase CT radiomics and deep transfer learning model for oxaliplatin response in unresectable colorectal liver metastases.

OBJECTIVES: Accurate prediction of response to first-line oxaliplatin-based chemotherapy in unresectable colorectal liver metastases (CRLM) is critica...

HPCSMN: A Classification Method of Chemotherapy Sensitivity of Hypopharyngeal Cancer Based on Multimodal Network.

The treatment of hypopharyngeal cancer faces complex challenges, and accurate prediction of chemotherapy sensitivity is crucial for personalized treat...

Preoperative prediction of cervical cancer recurrence using explainable MRI radiomics: a SHAP-Guided machine learning study.

PURPOSE: To develop and validate a machine learning model that integrated MRI radiomics features and clinical factors for preoperative prediction of p...

Deep learning-assisted SERS platform for label-free detection of celecoxib in serum using ag@Pt@porous silicon Bragg mirror composite substrate.

Celecoxib (CXB) is a non-steroidal anti-inflammatory drug used to prevent and treat arthritis. However, overdosing or improper use can cause adverse r...

Enhancing Severe Neutropenia Prediction: PKPD-Informed Labeling for Machine Learning Models Trained on Real-World Data.

Accurately labeling outcomes in real-world data for machine learning is challenging due to data sparsity and imbalances. This study developed and eval...