Other Cancers

Refining the Classroom: The Self-Supervised Professor Model for Improved Segmentation of Locally Advanced Pancreatic Ductal Adenocarcinoma.

Pancreatic ductal adenocarcinoma (PDAC) is a leading cause of cancer-related deaths, with accurate staging being critical for treatment planning. Automated 3D segmentation models can aid in staging, but segmenting PDAC, especially in cases of locally advanced pancreatic cancer (LAPC), is challenging due to the tumor's heterogeneous appearance, irregular shapes, and extensive infiltration. This stu...

HAMIL: Hierarchical Attention Multi-Instance Learning for Label-Free Colorectal Cancer Typing.

Colorectal cancer (CRC) is one of the leading gastrointestinal malignancies, underscoring the need for an in-depth analysis of the cellular within the tumor microenvironment. While pathological imaging remains the gold standard for cancer diagnosis, it requires extensive annotation time and expert knowledge. Therefore, we propose hierarchical attention multi-instance learning (HAMIL) for label-fre...

Bias or Best Fit? A Comparative Analysis of the SEER and NCDB Data Sets in Single-model Machine Learning for Predicting Osteosarcoma Survival Outcomes.

BACKGROUND: Machine-learning models are increasingly used in orthopaedic oncology to predict survival outcomes for patients with osteosarcoma. Typical...

Multitask radioclinical decision stratification in non-metastatic colon cancer: integrating MMR status, pT staging, and high-risk pathological factors.

OBJECTIVES: Constructing a multi-task global decision support system based on preoperative enhanced CT features to predict the mismatch repair (MMR) s...

Long-Term Outcomes and Cost-Effectiveness of Artificial Intelligence for Breast Cancer Screening: A Modeling Study.

OBJECTIVES: Artificial intelligence (AI)-assisted breast cancer screening may improve diagnostic accuracy; however, the long-term health outcomes and ...

Prognosis models for nasopharyngeal carcinoma recurrences by using tabu search algorithm.

Cancer is a significant public health issue that has a global impact. Significant mortality rates have already been observed due to this disease, and ...

Lightweight Transfer Learning Models for Multi-Class Brain Tumor Classification: Glioma, Meningioma, Pituitary Tumors, and No Tumor MRI Screening.

Glioma, pituitary tumors, and meningiomas constitute the major types of primary brain tumors. The challenge in achieving a definitive diagnosis stem f...

Exosomal biomarkers in cancer: Insights from Multi-OMIC approaches.

Extracellular vesicles, particularly exosomes, are emerging as powerful tools in cancer research due to their role in intercellular communication and ...

Current State-of-the-Art 3D MRI Sequences for Assessing Bone Morphology with Emphasis on Cranial and Spinal Imaging: A Narrative Review.

Traditionally, CT has been the go-to method for visualizing bone structures, while MRI has been preferred for assessing soft tissues, because structur...

Habitat-aware radiomics and adaptive 2.5D deep learning predict treatment response and long-term survival in ESCC patients undergoing neoadjuvant chemoimmunotherapy.

PURPOSE: Current radiomic approaches inadequately resolve spatial intratumoral heterogeneity (ITH) in esophageal squamous cell carcinoma (ESCC), limit...

Nivolumab plus ipilimumab for potentially resectable hepatocellular carcinoma: Long-term efficacy and biomarker exploration.

BACKGROUND & AIMS: Immune checkpoint inhibitor-based combination therapy has demonstrated high objective response rates in patients with hepatocellula...

A Metabolism-Driven Prognostic Model and PSMD14-SP1-GYS1 Axis Reveal Therapeutic Vulnerabilities in Melanoma.

Melanoma is a highly aggressive cutaneous malignancy characterized by a strong propensity for metastasis and therapy resistance, with its progression ...

Prospective clinical validation of a novel artificial intelligence system for real-time detection of solid pancreatic masses during endoscopic ultrasonography.

Endoscopic ultrasonography (EUS) is the most sensitive modality for accurately establishing a tissue diagnosis in patients with solid pancreatic masse...

Decoding the gut microbiota metabolite-matrix metalloproteinase-3 axis in breast cancer: a multi-omics and network pharmacology study.

Breast cancer is a malignant tumor originating from the breast epithelium, and emerging evidence suggests that the gut microbiota influences its devel...

Machine learning models for predicting recurrence and malignant transformation of oral leukoplakia.

OBJECTIVE: This study aimed to evaluate machine learning models for predicting the recurrence and malignant transformation of oral leukoplakia (OL). M...

Evaluation of the Detectability of Oral Potentially Malignant Diseases with a Deep Learning Approach: A Retrospective Pilot Study.

Oral potentially malignant diseases (OPMD) may arise during the malignant transformation of the oral mucosa, with cellular changes in these lesions in...

Artificial intelligence in gastric cancer: a systematic review of machine learning and deep learning applications.

BACKGROUND: Gastric cancer (GC) remains a major global health concern, ranking as the fifth most prevalent malignancy and the fourth leading cause of ...

Prognostic Utility of a Deep Learning Radiomics Nomogram Integrating Ultrasound and Multi-Sequence MRI in Triple-Negative Breast Cancer Treated With Neoadjuvant Chemotherapy.

OBJECTIVE: The aim of this study is to evaluate the prognostic performance of a nomogram integrating clinical parameters with deep learning radiomics ...

Harnessing biomarkers to guide immunotherapy in esophageal cancer: toward precision oncology.

Esophageal cancer (EC) is one of the most serious health issues around the world, ranking seventh among the most lethal types of cancer and eleventh a...

Artificial intelligence-assisted assessment of metabolic response to tebentafusp in metastatic uveal melanoma: a long axial field-of-view [18F]FDG PET/CT study.

PURPOSE: Tebentafusp has emerged as the first systemic therapy to significantly prolong survival in treatment-naïve HLA-A*02:01 + patients with unrese...