Breast Cancer

Explainable machine learning to compare the overall survival status between patients receiving mastectomy and breast conserving surgeries.

The most prevalent malignancy among women is breast cancer; hence, treatment approaches are needed in consideration of tumor characteristics and disease stage but also patient preference. Two surgical options, Mastectomy and Breast Conserving Surgery (BCS), share the same survival outcomes, clinical or molecular factors; and explainable Machine Learning (ML) techniques like SHapley Additive exPlan...

Amogel: a multi-omics classification framework using associative graph neural networks with prior knowledge for biomarker identification.

The advent of high-throughput sequencing technologies, such as DNA microarray and DNA sequencing, has enabled effective analysis of cancer subtypes and targeted treatment. Furthermore, numerous studies have highlighted the capability of graph neural networks (GNN) to model complex biological systems and capture non-linear interactions in high-throughput data. GNN has proven to be useful in leverag...

Deep Learning-Based Auto-Segmentation for Liver Yttrium-90 Selective Internal Radiation Therapy.

The aim was to evaluate a deep learning-based auto-segmentation method for liver delineation in Y-90 selective internal radiation therapy (SIRT). A de...

Control of dental calculus Prevents severe Radiation-Induced oral mucositis in patients undergoing radiotherapy for nasopharyngeal carcinoma.

PURPOSE: This study aims to develop an artificial intelligence model to predict severe radiation-induced oral mucositis (RIOM) in patients with locall...

Toward Accurate Deep Learning-Based Prediction of Ki67, ER, PR, and HER2 Status From H&E-Stained Breast Cancer Images.

Despite improvements in machine learning algorithms applied to digital pathology, only moderate accuracy, to predict molecular information from histol...

Enhancing prediction and stratifying risk: machine learning and bayesian-learning models for catheter-related thrombosis in chemotherapy patients.

BACKGROUND: Catheter-related thrombosis (CRT) is a serious complication in cancer patients undergoing chemotherapy, yet existing risk prediction model...

Individualized Analysis of Nipple-Sparing Mastectomy Versus Modified Radical Mastectomy Using Deep Learning.

BACKGROUND: This study aimed to evaluate the impact of nipple-sparing mastectomy (NSM) and modified radical mastectomy (MRM) on individual survival ou...

Explainable AI-based feature importance analysis for ovarian cancer classification with ensemble methods.

INTRODUCTION: Ovarian Cancer (OC) is one of the leading causes of cancer deaths among women. Despite recent advances in the medical field, such as sur...

Optimizing imaging modalities for sarcoma subtypes in radiation therapy: State of the art.

The choice of imaging modalities is essential in sarcoma management, as different techniques provide complementary information depending on tumor subt...

Nanomaterial-Based Molecular Imaging in Cancer: Advances in Simulation and AI Integration.

Nanomaterials represent an innovation in cancer imaging by offering enhanced contrast, improved targeting capabilities, and multifunctional imaging mo...

American College of Veterinary Radiology and European College of Veterinary Diagnostic Imaging position statement on artificial intelligence.

The American College of Veterinary Radiology (ACVR) and the European College of Veterinary Diagnostic Imaging (ECVDI) recognize the transformative pot...

Multimodal treatment of colorectal liver metastases: Where are we? Current strategies and future perspectives.

Despite the continued high prevalence of colorectal cancer in the Western world, recent years have witnessed a decline in its mortality rate, largely ...

Utility of comprehensive genomic profiling combined with machine learning for prognostic stratification in stage II/III colorectal cancer after adjuvant chemotherapy.

BACKGROUND AND PURPOSE: Accurate recurrence risk evaluation in patients with stage II and III colorectal cancer (CRC) remains difficult. Traditional h...

DPI-MoCo: Deep Prior Image Constrained Motion Compensation Reconstruction for 4D CBCT.

4D cone-beam computed tomography (CBCT) plays a critical role in adaptive radiation therapy for lung cancer. However, extremely sparse sampling projec...

AI Enhanced explainable early prediction of blood culture positivity in neutropenic patients using clinical and hematologic parameters.

Leukemia patients who receive chemotherapy experience a decline in neutrophils and an increased risk of infections. Neutropenic sepsis is a life-threa...

Voxel-level radiomics and deep learning for predicting pathologic complete response in esophageal squamous cell carcinoma after neoadjuvant immunotherapy and chemotherapy.

BACKGROUND: Accurate prediction of pathologic complete response (pCR) following neoadjuvant immunotherapy combined with chemotherapy (nICT) is crucial...

Unsupervised learning to identify symptom clusters in older adults undergoing chemotherapy.

INTRODUCTION: Unsupervised machine learning (ML) approaches such as clustering have not been commonly applied to patient-reported data. This study des...

Prediction of Chemotherapy Response in Locally Advanced Breast Cancer Patients at Pre-Treatment Using CT Textural Features and Machine Learning: Comparison of Feature Selection Methods.

RATIONALE: Neoadjuvant chemotherapy (NAC) is a key element of treatment for locally advanced breast cancer (LABC). Predicting the response of NAC for ...

Low-dose CT reconstruction using cross-domain deep learning with domain transfer module.

. X-ray computed tomography employing low-dose x-ray source is actively researched to reduce radiation exposure. However, the reduced photon count in ...

Dual-Modality Virtual Biopsy System Integrating MRI and MG for Noninvasive Predicting HER2 Status in Breast Cancer.

RATIONALE AND OBJECTIVES: Accurate determination of human epidermal growth factor receptor 2 (HER2) expression is critical for guiding targeted therap...