Oncology/Hematology

Other Cancers

Latest AI and machine learning research in other cancers for healthcare professionals.

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Development of a prognostic model for NSCLC based on differential genes in tumour stem cells.

Non-small cell lung cancer (NSCLC) constitutes a significant portion of lung cancers and cytotoxic d...

Impact of SUSAN Denoising and ComBat Harmonization on Machine Learning Model Performance for Malignant Brain Neoplasms.

BACKGROUND AND PURPOSE: Feature variability in radiomics studies due to technical and magnet strengt...

Development of a novel prognostic signature derived from super-enhancer-associated gene by machine learning in head and neck squamous cell carcinoma.

Dysregulated super-enhancer (SE) results in aberrant transcription that drives cancer initiation and...

Metabolic phenotyping combined with transcriptomics metadata fortifies the diagnosis of early-stage Hepatocellular carcinoma.

INTRODUCTION: The low sensitivity of alpha-fetoprotein (AFP) renders it unsuitable as a stand-alone ...

The utility and reliability of a deep learning algorithm as a diagnosis support tool in head & neck non-melanoma skin malignancies.

OBJECTIVE: The incidence of non-melanoma skin cancers, encompassing basal cell carcinoma (BCC) and c...

Imatinib adherence prediction using machine learning approach in patients with gastrointestinal stromal tumor.

BACKGROUND: Nonadherence to imatinib is common in patients with gastrointestinal stromal tumor (GIST...

In silico assessments of the small molecular boron agents to pave the way for artificial intelligence-based boron neutron capture therapy.

Boron neutron capture therapy (BNCT) is a highly targeted, selective and effective technique to cure...

Precision meets generalization: Enhancing brain tumor classification via pretrained DenseNet with global average pooling and hyperparameter tuning.

Brain tumors pose significant global health concerns due to their high mortality rates and limited t...

Evaluating the Alignment of Artificial Intelligence-Generated Recommendations With Clinical Guidelines Focused on Soft Tissue Tumors.

BACKGROUND: The integration of artificial intelligence (AI), particularly, in oncology, has signific...

PSEENet: A Pseudo-Siamese Neural Network Incorporating Electroencephalography and Electrooculography Characteristics for Heterogeneous Sleep Staging.

Sleep staging plays a critical role in evaluating the quality of sleep. Currently, most studies are ...

The impact of deep learning on diagnostic performance in the differentiation of benign and malignant thyroid nodules.

AIMS: This study aims to use deep learning (DL) to classify thyroid nodules as benign and malignant ...

Brain tumor detection and segmentation using deep learning.

OBJECTIVES: Brain tumor detection, classification and segmentation are challenging due to the hetero...

Deep Learning for Distinguishing Mucinous Breast Carcinoma From Fibroadenoma on Ultrasound.

PURPOSE: Mucinous breast carcinoma (MBC) tends to be misdiagnosed as fibroadenomas (FA) due to its b...

Artificial intelligence for ultrasonographic detection and diagnosis of hepatocellular carcinoma and cholangiocarcinoma.

The effectiveness of ultrasonography (USG) in liver cancer screening is partly constrained by the op...

Brain tumor image segmentation method using hybrid attention module and improved mask RCNN.

To meet the needs of automated medical analysis of brain tumor magnetic resonance imaging, this stud...

Comparison of Pathologist and Artificial Intelligence-based Grading for Prediction of Metastatic Outcomes After Radical Prostatectomy.

Gleason grade group (GG) is the most powerful prognostic variable in localized prostate cancer; howe...

Machine Learning for Targeted Advance Care Planning in Cancer Patients: A Quality Improvement Study.

CONTEXT: Prognostication challenges contribute to delays in advance care planning (ACP) for patients...

Bone metastasis scintigram generation using generative adversarial learning with multi-receptive field learning and two-stage training.

BACKGROUND: Deep learning is the primary method for conducting automated analysis of SPECT bone scin...

Deep learning method for predicting weekly anatomical changes in patients with nasopharyngeal carcinoma during radiotherapy.

BACKGROUND: Patients may undergo anatomical changes during radiotherapy, leading to an underdosing o...

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