Lung Cancer

From planning to prognosis: predicting renal function after minimally-invasive partial nephrectomy with artificial intelligence.

This study presents a machine learning model to predict renal function decline following minimally-invasive partial nephrectomy. Using a dataset of 556 patients treated between 2015 and 2023, the model incorporated patient, tumor, and intraoperative surgical variables - including clamping strategy, resection technique, and renorrhaphy type - to estimate the 3-month postoperative eGFR drop. A Rando...

Automated field-in-field planning for tangential breast radiation therapy based on digitally reconstructed radiograph.

BACKGROUND: The tangential field-in-field (FIF) technique is a widely used method in breast radiation therapy, known for its efficiency and the reduced number of fields required in treatment planning. However, it is labor-intensive, requiring manual shaping of the multileaf collimator (MLC) to minimize hot spots.

Radiation oncology patients' perceptions of artificial intelligence and machine learning in cancer care: A multi-centre cross-sectional study.

AIM: The use of artificial intelligence (AI) and machine learning (ML) is increasingly widespread in radiation oncology. However, patient engagement t...

Ultra-Sparse-View Cone-Beam CT Reconstruction-Based Strictly Structure-Preserved Deep Neural Network in Image-Guided Radiation Therapy.

Radiation therapy is regarded as the mainstay treatment for cancer in clinic. Kilovoltage cone-beam CT (CBCT) images have been acquired for most treat...

Immunohistochemistry and machine learning study of DNA replication-associated proteins in uterine epithelial tumors and precursor lesions.

Endometrioid adenocarcinoma (EA) has been on the increase in recent years in developed countries. Early detection of endometrioid adenocarcinoma in th...

Wrist and elbow fracture detection and segmentation by artificial intelligence using point-of-care ultrasound.

PURPOSE: Distal radius (wrist) and supracondylar (elbow) fractures are common in children presenting to Pediatric Emergency Departments (EDs). These f...

Pathway Enrichment-Based Unsupervised Learning Identifies Novel Subtypes of Cancer-Associated Fibroblasts in Pancreatic Ductal Adenocarcinoma.

Existing single-cell clustering methods are based on gene expressions that are susceptible to dropout events in single-cell RNA sequencing (scRNA-seq)...

CellOMaps: A compact representation for robust classification of lung adenocarcinoma growth patterns.

Lung adenocarcinoma (LUAD) is a morphologically heterogeneous disease, characterized by five primary histological growth patterns. The classification ...

Dimensional Synthesis of 6-DOF Parallel Robot for Intra-Operative Radiation Therapy.

BACKGROUND: In order to meet the kinematic requirements of large range of motion, payload, and stiffness of Intra-Operative Radiation Therapy robots, ...

Prediction methodology of air absorbed dose rates for Chinese cities with deep learning models.

Air absorbed dose rate is a key indicator of environmental radiation exposure. In China, automated environmental radiation monitoring systems have bee...

Design and molecular mechanism investigation of ALK inhibitors based on virtual screening and structural descriptor modeling.

To address the challenges of target specificity and drug resistance in Anaplastic lymphoma kinase (ALK) inhibition, this study conducted a virtual scr...

Driving Knowledge to Action: Building a Better Future With Artificial Intelligence-Enabled Multidisciplinary Oncology.

Artificial intelligence (AI) is transforming multidisciplinary oncology at an unprecedented pace, redefining how clinicians detect, classify, and trea...

Artificial intelligence in predicting EGFR mutations from whole slide images in lung Cancer: A systematic review and Meta-Analysis.

BACKGROUND: Epidermal growth factor receptor (EGFR) mutations play a pivotal role in guiding targeted therapy for lung cancer, making their accurate d...

A machine learning-derived angiogenesis signature for clinical prognosis and immunotherapy guidance in colon adenocarcinoma.

Colon adenocarcinoma (COAD) is one of the most prevalent malignancies worldwide and its prognosis is extremely poor. Angiogenesis has been linked to c...

Utilizing spectral detector computed tomography quantitative parameters via multimodality tumor tracking to predict occult lymph node metastasis in clinical stage 1 pure solid lung adenocarcinoma.

BACKGROUND: The presence of occult lymph node metastasis (OLNM) has significant implications for the staging, treatment and prognosis of patients with...

Deep learning in histopathology images for prediction of oncogenic driver molecular alterations in lung cancer: a systematic review and meta-analysis.

BACKGROUND: Lung cancer (LC) is the second most diagnosed cancer and the leading cause of cancer mortality worldwide. Non-small cell lung cancer (NSCL...

ImmunoDiff: A Diffusion Model for Immunotherapy Response Prediction in Lung Cancer

Accurately predicting immunotherapy response in Non-Small Cell Lung Cancer (NSCLC) remains a critical unmet need. Existing radiomics and deep learni...

Comparative Analysis of Machine Learning Models for Lung Cancer Mutation Detection and Staging Using 3D CT Scans

Lung cancer is the leading cause of cancer mortality worldwide, and non-invasive methods for detecting key mutations and staging are essential for i...

Harnessing artificial intelligence to address immune response heterogeneity in low-dose radiation therapy.

Low-dose radiation therapy has emerged as a promising modality for cancer treatment because of its ability to stimulate antitumor immune responses whi...

Deep learning radiomics fusion model to predict visceral pleural invasion of clinical stage IA lung adenocarcinoma: a multicenter study.

AIM: To assess the predictive performance, risk stratification capabilities, and auxiliary diagnostic utility of radiomics, deep learning, and fusion ...