Breast Cancer

Dosimetric parameter-based machine learning to predict radiation pneumonitis following breast radiotherapy.

Objective.Radiation pneumonitis (RP) is an important toxicity following breast radiotherapy. Although modern treatment techniques limit lung exposure, some patients still develop symptomatic RP, and predicting who is at risk remains difficult. This study aimed to develop and validate machine learning (ML) models based on ipsilateral-lung dosimetric parameters to predict clinically significant RP a...

Building Machine Learning Models Based on Oxidative Stress Index Score to Predict Survival of Locally Advanced Rectal Cancer Receiving Different Neoadjuvant Therapy Patterns.

BACKGROUND: Liver enzyme biomarkers are known to contribute to the onset and progression of colorectal cancer. OBJECTIVE: To develop a novel oxidative stress index score integrating γ-glutamyl transferase and total bilirubin, evaluate its prognostic value in locally advanced rectal cancer patients receiving neoadjuvant therapy, and construct and validate machine learning-based survival prediction ...

Validation of tumour-negative lymph node size as a novel independent prognostic biomarker in oesophagogastric adenocarcinoma: post hoc analysis of the UK MRC OE05 and ST03 randomised trials.

BACKGROUND: The size of tumour-negative regional lymph nodes (LNnegs) may be associated with the host antitumour immune response in patients with oeso...

Biologically adaptive radiation oncology in oligometastatic and oligoprogressive disease: MR- and CBCT-guided adaptation, molecular imaging, dose painting, biomarkers, and AI.

Oligometastatic disease (OMD) and oligoprogression are clinically actionable states in which local treatment of limited tumour burden can prolong dise...

From Andes to the dry diagonal: phylogenomics and trait evolution elucidate the radiation of cacti in South American drylands.

The expansion of arid and dry ecosystems during the Miocene played a key role in the diversification of succulent plants; however, the relative contri...

Deep learning for CT-less attenuation correction synthesis in non-small cell lung cancer: A proof-of-concept evaluated for quantitative and physical characteristics.

To develop and rigorously validate a deep learning framework for CT-free positron emission tomography (PET) attenuation correction in non-small cell l...

Ultra-low dose chest-abdomen-pelvis CT with deep-learning image reconstruction for cancer follow-up: Impact on image quality and lesion detection.

PURPOSE: The purpose of this study was to compare the image quality and lesion detection between ultra-low dose (ULD) chest-abdomen-pelvis computed to...

Modeling and forecasting oyster norovirus outbreaks of long-range dependence on antecedent marine environmental conditions.

This study presents three artificial intelligence-based models - XGBoost, Random Forest (RF), and Deep Artificial Neural Network (DANN) - with 2-day l...

Development and validation of a computational histology artificial intelligence-powered prognostic biomarker in muscle-invasive bladder cancer.

BACKGROUND: Patients with muscle-invasive bladder cancer (MIBC) have heterogeneous outcomes following transurethral resection of bladder tumor (TURBT)...

A clinical-radiomics model based on MRI sub-regions of gluteus maximus for recurrence prediction in high-grade serous ovarian cancer.

PURPOSE: To evaluate the predictive value of MRI sub-regional radiomics of the gluteus maximus for recurrence in high-grade serous ovarian cancer (HGS...

Prediction models for adverse events from prostate cancer curative radiotherapy: a systematic review of methodological quality.

BACKGROUND: Predicting risks of urinary, bowel, sexual, and other adverse effects following prostate cancer curative radiotherapy (PCa-RT) is essentia...

Interfractional Monitoring of Spacer Hydrogel Morphology in Patients With Prostate Cancer Receiving SBRT Treatment.

PURPOSE: The purpose of this study is to explore whether spacer hydrogel morphology changes during the course of stereotactic body radiation therapy (...

Local recurrence in rectal cancer: from detection to structured reporting.

Despite advances in total mesorectal excision and neoadjuvant therapy, locally recurrent rectal cancer remains a clinically important source of pelvic...

Comment on "Would You Like a Side of Artificial Intelligence With Your Mammogram? An Experimental Study of Patient Willingness to Pay for Artificial Intelligence".

Pharmacogenomic biomarkers in oncology: evidence, clinical utility, and barriers to implementation.

Interpatient variability in chemotherapy response and toxicity remains a major challenge in oncology. Pharmacogenomics (PGx) is an approach to address...

A proof-of-concept study of optical tissue sensing during vacuum-assisted breast biopsy procedures.

BACKGROUND: Vacuum-assisted breast biopsy (VABB) enables minimally invasive sampling of breast lesions but provides no real-time information about the...

Machine learning-inspired design of a modified spider-shaped MIMO antenna for 5G-advanced wireless communications.

A modified Spider Shaped Four Element Multi Input Multi Output Antenna (SSFEMIMOA) is suggested for the 5G-advanced, and sub-6 GHz advanced wireless c...

Radiation dose optimization and dosimetry in pediatric nuclear medicine: Current concepts and future directions.

Pediatric nuclear medicine plays an essential role in the diagnosis and treatment of a wide range of oncologic and non-oncologic diseases while requir...

Radiomics Model Predicts Efficacy of First-Line CDK4/6 Inhibitor Combined with Endocrine Therapy in HR+/HER2- Advanced Breast Cancer.

INTRODUCTION: The combination of CDK4/6 inhibitors and endocrine therapy is the first-line standard regimen for advanced HR⁺/HER₂⁻ breast cancer. Howe...

Innovations and Future Directions in the Management of Eustachian Tube Dysfunction.

Innovations in Eustachian tube dysfunction (ETD) management are shifting care from symptom-based approaches toward precision, technology-integrated st...