Chemotherapy

The Promise of Machine Learning-Guided Adjuvant Chemotherapy for Resected Gallbladder Cancer.

Integrating MRI radiomics and deep learning for predicting pathologic complete response to neoadjuvant chemotherapy in breast cancer: a multicenter study.

OBJECTIVE: Develop a multimodal fusion model combining MRI radiomics and deep learning (DL) to predict pathologic complete response (pCR) in breast cancer patients post-neoadjuvant chemotherapy (NACT). METHODS: Patients with locally advanced breast cancer from two centers (Center 1: 404 training, Center 2: 174 testing) were enrolled. Pretreatment MRI radiomic features and DL-derived features were ...

A Radiomics-Driven Model to Distinguish Between Clinically Similar Myxopapillary Ependymomas and Lumbosacral Schwannomas.

BACKGROUND AND OBJECTIVES: Myxopapillary ependymomas (MPE) and intradural lumbosacral schwannomas may be challenging to distinguish based on presentin...

Pharmacogenomics in oncology: mutation-targeted therapy and biomarker integration in non-small cell lung cancer.

Non-small cell lung cancer (NSCLC) is the most common cancer-related cause of death among all countries globally, mostly because of late diagnosis, he...

Activated Cancer-Associated Fibroblasts Are Associated with Immunosuppression and Poor Prognosis in Clear Cell Renal Cell Carcinoma.

INTRODUCTION: Clear cell renal cell carcinoma (ccRCC) exhibits substantial heterogeneity within its tumor microenvironment, contributing to variable c...

Treatment-specific CT Radiomics Models To Predict Response To Neoadjuvant Therapy And Explore Individualized Treatment Selection In Gastric Cancer.

RATIONALE AND OBJECTIVES: To develop and compare general and treatment-specific radiomics models based on pretreatment computed tomography (CT) for pr...

Reframing Response in Neoadjuvant Chemotherapy for Ovarian Cancer: Integrating Molecular and Functional Biomarkers.

BACKGROUND: Neoadjuvant chemotherapy (NACT) is an established treatment strategy for advanced epithelial ovarian cancer, particularly for patients wit...

Topological imaging of single-cell CAF heterogeneity for predicting prognosis and adjuvant chemotherapy benefit in pancreatic cancer.

PURPOSE: Cancer-associated fibroblasts (CAFs) are central drivers of PDAC progression and therapeutic resistance, yet their preoperative clinical util...

A machine learning-driven multitask framework for antibiotic resistance prediction in Escherichia coli, Klebsiella pneumoniae, and Enterobacter cloacae bloodstream infections among hematological malignancy patients.

BACKGROUND: Inappropriate empirical antimicrobial therapy (IEAT) significantly increases mortality in patients with resistant Gram-negative bacteremia...

AI-Optimized 3D-Printed Dual-Layer Implant: Resolving Chemo-Immunotherapy Sequential Administration Dilemma for Colorectal Cancer Peritoneal Metastasis.

The synergistic integration of chemotherapy and immunotherapy represents the most promising strategy for enhancing therapeutic efficacy in cancer trea...

Automatic recognition of anatomical structures and surgical phases in robot-assisted minimally invasive esophagectomy (RAMIE) using deep learning: a retrospective cohort study.

BACKGROUND: Curative treatment of resectable esophageal cancer comprises neoadjuvant chemoradiotherapy and esophagectomy. Robot-assisted minimally inv...

Current progress and future directions of intraoperative frozen section for spread through air spaces in lung cancer.

Spread through air spaces (STAS) is a recently recognized pattern of invasion in lung cancer that is strongly linked to postoperative recurrence and p...

Macrophage-Induced Senescent Cancer-Associated Fibroblasts Promote SASP-Mediated Chemoresistance in Colorectal Cancer.

Cancer-associated fibroblasts (CAFs) play a crucial role in the tumor microenvironment (TME) by influencing tumor progression, metastasis, and therapy...

Minimally invasive circulating MicroRNA signatures for breast cancer detection and neoadjuvant therapy response prediction in a multicenter MENA cohort.

BACKGROUND: Early detection and treatment stratification of breast cancer remain major clinical challenges, particularly in the Middle East and North ...

Gramado consensus on imaging evaluation of the response to neoadjuvant systemic therapy for breast cancer.

The assessment of response to neoadjuvant systemic therapy (NST) is a critical pillar in defining the multidisciplinary therapeutic strategy in breast...

Development and validation of a machine learning model to preoperatively predict very early recurrence in patients with hepatitis B virus-related hepatocellular carcinoma.

Very early recurrence (within one year) after curative hepatectomy significantly impairs long-term survival in patients with hepatitis B virus (HBV)-r...

PANDA pediatric arousal neural detection architecture.

We developed and validated PANDA, a deep-learning model for pediatric arousal detection in polysomnography. PANDA uses a U-Net-style encoder-decoder a...

Comparative outcomes of proton and photon therapy in the management of malignant peripheral nerve sheath tumors.

BACKGROUND: Malignant Peripheral Nerve Sheath Tumors (MPNSTs) are aggressive sarcomas often arising in the spine, characterized by high metastatic pot...

Predicting sentinel lymph node metastasis in breast cancer using an interpretable machine learning approach based on multi-domain clinical features.

BACKGROUND: Accurate identification of sentinel lymph node (SLN) status is essential for surgical and adjuvant therapy decisions in breast cancer. Alt...

Interpretable machine learning model of contrast-enhanced CT radiomics for predicting post-BCG recurrence in high-grade non-muscle-invasive bladder cancer.

BACKGROUND AND PURPOSE: While intravesical Bacillus Calmette-Guérin Vaccine (BCG) instillation remains standard adjuvant therapy for high-grade non-mu...