Oncology/Hematology

Exploring patient stratification in head and neck squamous cell carcinoma using machine learning techniques: Preliminary results.

BACKGROUND: Head and Neck Squamous Cell Carcinoma (HNSCC) presents a significant challenge in oncology due to its inherent heterogeneity. Traditional staging systems, such as TNM (Tumor, Node, Metastasis), provide limited information regarding patient outcomes and treatment responses. There is a need for a more robust system to improve patient stratification.

FT-FEDTL: A fine-tuned feature-extracted deep transfer learning model for multi-class microwave-based brain tumor classification.

The microwave brain imaging (MBI) system is an emerging technology used to detect brain tumors in their early stages. Multi-class microwave-based brain tumor (MBT) identification and classification are crucial due to the tumor's patterns and shape. Manual identification and categorization of the tumors from the images by physicians is a challenging task and consumes more time. Recently, to overcom...

A deep learning approach for ovarian cancer detection and classification based on fuzzy deep learning.

Different oncologists make their own decisions about the detection and classification of the type of ovarian cancer from histopathological whole slide...

HiDDEN: a machine learning method for detection of disease-relevant populations in case-control single-cell transcriptomics data.

In case-control single-cell RNA-seq studies, sample-level labels are transferred onto individual cells, labeling all case cells as affected, when in r...

Enhancing MRI brain tumor classification: A comprehensive approach integrating real-life scenario simulation and augmentation techniques.

Brain cancer poses a significant global health challenge, with mortality rates showing a concerning surge over recent decades. The incidence of brain ...

From prediction to practice: mitigating bias and data shift in machine-learning models for chemotherapy-induced organ dysfunction across unseen cancers.

OBJECTIVES: Routine monitoring of renal and hepatic function during chemotherapy ensures that treatment-related organ damage has not occurred and clea...

NSSC: a neuro-symbolic AI system for enhancing accuracy of named entity recognition and linking from oncologic clinical notes.

Accurate recognition and linking of oncologic entities in clinical notes is essential for extracting insights across cancer research, patient care, cl...

Transformative artificial intelligence in gastric cancer: Advancements in diagnostic techniques.

Gastric cancer represents a significant global health challenge with elevated incidence and mortality rates, highlighting the need for advancements in...

Three-dimensional deep learning model complements existing models for preoperative disease-free survival prediction in localized clear cell renal cell carcinoma: a multicenter retrospective cohort study.

BACKGROUND: Current prognostic models have limited predictive abilities for the growing number of localized (stage I-III) ccRCCs. It is, therefore, cr...

Deep Learning and Automatic Differentiation of Pancreatic Lesions in Endoscopic Ultrasound: A Transatlantic Study.

INTRODUCTION: Endoscopic ultrasound (EUS) allows for characterization and biopsy of pancreatic lesions. Pancreatic cystic neoplasms (PCN) include muci...

Editorial: Artificial Intelligence (AI), Digital Image Analysis, and the Future of Cancer Diagnosis and Prognosis.

On October 8 2024, the Royal Swedish Academy of Sciences announced the 2024 Nobel Prize in Physics was awarded to Hopfield and Hinton for their founda...

Efficient brain tumor grade classification using ensemble deep learning models.

Detecting brain tumors early on is critical for effective treatment and life-saving efforts. The analysis of the brain with MRI scans is fundamental t...

Integrative hybrid deep learning for enhanced breast cancer diagnosis: leveraging the Wisconsin Breast Cancer Database and the CBIS-DDSM dataset.

The objective of this investigation was to improve the diagnosis of breast cancer by combining two significant datasets: the Wisconsin Breast Cancer D...

Deep learning-based automatic image classification of oral cancer cells acquiring chemoresistance in vitro.

Cell shape reflects the spatial configuration resulting from the equilibrium of cellular and environmental signals and is considered a highly relevant...

ACL-DUNet: A tumor segmentation method based on multiple attention and densely connected breast ultrasound images.

Breast cancer is the most common cancer in women. Breast masses are one of the distinctive signs for diagnosing breast cancer, and ultrasound is widel...

Machine Learning Differentiates Between Benign and Malignant Parotid Tumors With Contrast-Enhanced Ultrasound Features.

BACKGROUND: Contrast-enhanced ultrasound (CEUS) is frequently used to distinguish benign parotid tumors (BPTs) from malignant parotid tumors (MPTs). I...

Machine learning models reveal ARHGAP11A's impact on lymph node metastasis and stemness in NSCLC.

Most patients with non-small cell lung cancer (NSCLC) are diagnosed at an advanced stage of the disease, which complicates treatment due to a heighten...

Identification of ferroptosis-related genes associated with diffuse large B-cell lymphoma via bioinformatics and machine learning approaches.

Ferroptosis has emerged as a critical mechanism in the development and progression of various tumors, particularly diffuse large B-cell lymphoma (DLBC...

Innovative label-free lymphoma diagnosis using infrared spectroscopy and machine learning on tissue sections.

The diagnosis of lymphomas is challenging due to their diverse histological presentations and clinical manifestations. There is a need for inexpensive...

Construction of a combined prognostic model for pancreatic ductal adenocarcinoma based on deep learning and digital pathology images.

BACKGROUND: Deep learning has made significant advancements in the field of digital pathology, and the integration of multiple models has further impr...