Oncology/Hematology

Analysis of international publication trends in artificial intelligence in skin cancer.

Bibliometric methods were used to analyze publications on the use of artificial intelligence (AI) in skin cancer from 2010 to 2022, aiming to explore current publication trends and future directions. A comprehensive search using four terms, "artificial intelligence," "machine learning," "deep learning," and "skin cancer," was performed in the Web of Science database for original English language p...

Severe degranulation of mesenteric mast cells in an experimental rat mammary tumor model.

BACKGROUND/AIM: Breast cancers are one of the most common cancers in women and are responsible for many deaths worldwide. Mast cells are inflammatory cells. Their role in cancers is controversial, and there is limited data on systemic mast cell activation in cancer cases. This study aimed to evaluate systemic mast cell activation in an experimentally induced rat model of breast cancer.

Artificial intelligence system for identification of overlooked lung metastasis in abdominopelvic computed tomography scans of patients with malignancy.

PURPOSE: This study aimed to evaluate whether an artificial intelligence (AI) system can identify basal lung metastatic nodules examined using abdomin...

Implementing machine learning to predict survival outcomes in patients with resected pulmonary large cell neuroendocrine carcinoma.

BACKGROUND: The post-surgical prognosis for Pulmonary Large Cell Neuroendocrine Carcinoma (PLCNEC) patients remains largely unexplored. Developing a p...

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 strength parameters is well-known and may be addressed th...

MR Cranial Bone Imaging: Evaluation of Both Motion-Corrected and Automated Deep Learning Pseudo-CT Estimated MR Images.

BACKGROUND AND PURPOSE: CT imaging exposes patients to ionizing radiation. MR imaging is radiation free but previously has not been able to produce di...

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 drugs (e.g. cisplatin) are currently the first-line...

A Multi-Scale Liver Tumor Segmentation Method Based on Residual and Hybrid Attention Enhanced Network with Contextual Integration.

Liver cancer is one of the malignancies with high mortality rates worldwide, and its timely detection and accurate diagnosis are crucial for improving...

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 progression. SEs have been demonstrated as novel ...

A time-dependent explainable radiomic analysis from the multi-omic cohort of CPTAC-Pancreatic Ductal Adenocarcinoma.

BACKGROUND AND OBJECTIVE: In Pancreatic Ductal Adenocarcinoma (PDA), multi-omic models are emerging to answer unmet clinical needs to derive novel qua...

Application of Machine Learning Algorithms for Risk Stratification and Efficacy Evaluation in Cervical Cancer Screening among the ASCUS/LSIL Population: Evidence from the Korean HPV Cohort Study.

PURPOSE: We assessed human papillomavirus (HPV) genotype-based risk stratification and the efficacy of cytology testing for cervical cancer screening ...

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 cutaneous squamous cell carcinoma (cSCC), is on the...

Low-dose computed tomography perceptual image quality assessment.

In computed tomography (CT) imaging, optimizing the balance between radiation dose and image quality is crucial due to the potentially harmful effects...

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 various types of cancers, with less harm to the h...

Clinical implementation of deep learning robust IMPT planning in oropharyngeal cancer patients: A blinded clinical study.

BACKGROUND AND PURPOSE: This study aimed to evaluate the plan quality of our deep learning-based automated treatment planning method for robustly opti...

Leveraging AI and patient metadata to develop a novel risk score for skin cancer detection.

Melanoma of the skin is the 17th most common cancer worldwide. Early detection of suspicious skin lesions (melanoma) can increase 5-year survival rate...

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 treatment options. These tumors, arising from abnor...

Exploring a method for extracting concerns of multiple breast cancer patients in the domain of patient narratives using BERT and its optimization by domain adaptation using masked language modeling.

Narratives posted on the internet by patients contain a vast amount of information about various concerns. This study aimed to extract multiple concer...

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 significantly shifted the paradigms of medical diagnostics...

Data privacy-aware machine learning approach in pancreatic cancer diagnosis.

PROBLEM: Pancreatic ductal adenocarcinoma (PDAC) is considered a highly lethal cancer due to its advanced stage diagnosis. The five-year survival rate...