Oncology/Hematology

Application of molecular dynamics-based pharmacophore and machine learning approaches to identify novel Mcl1 inhibitors through drug repurposing and mechanics research.

Myeloid cell leukemia 1 (Mcl1), a critical protein that regulates apoptosis, has been considered as a promising target for antitumor drugs. The conventional pharmacophore screening approach has limitations in conformation sampling and data mining. Here, we offered an innovative solution to identify Mcl1 inhibitors with molecular dynamics-refined pharmacophore and machine learning methods. Consider...

Ultrasound contrast-enhanced radiomics model for preoperative prediction of the tumor grade of clear cell renal cell carcinoma: an exploratory study.

BACKGROUND: This study aims to explore machine learning(ML) methods for non-invasive assessment of WHO/ISUP nuclear grading in clear cell renal cell carcinoma(ccRCC) using contrast-enhanced ultrasound(CEUS) radiomics.

SCAC: A Semi-Supervised Learning Approach for Cervical Abnormal Cell Detection.

Cervical abnormal cell detection plays a crucial role in the early screening of cervical cancer. In recent years, some deep learning-based methods hav...

Deep Learning Models of Multi-Scale Lesion Perception Attention Networks for Diagnosis and Staging of Pneumoconiosis: A Comparative Study with Radiologists.

Accurate prediction of pneumoconiosis is essential for individualized early prevention and treatment. However, the different manifestations and high h...

MicroRNAs: circulating biomarkers for the early detection of imperceptible cancers via biosensor and machine-learning advances.

This review explores the topic of microRNAs (miRNAs) for improved early detection of imperceptible cancers, with potential to advance precision medici...

A multi-institutional machine learning algorithm for prognosticating facial nerve injury following microsurgical resection of vestibular schwannoma.

Vestibular schwannomas (VS) are the most common tumor of the skull base with available treatment options that carry a risk of iatrogenic injury to the...

Machine learning survival prediction using tumor lipid metabolism genes for osteosarcoma.

Osteosarcoma is a primary malignant tumor that commonly affects children and adolescents, with a poor prognosis. The existence of tumor heterogeneity ...

Deep learning-based pathway-centric approach to characterize recurrent hepatocellular carcinoma after liver transplantation.

BACKGROUND: Liver transplantation (LT) is offered as a cure for Hepatocellular carcinoma (HCC), however 15-20% develop recurrence post-transplant whic...

Pairwise machine learning-based automatic diagnostic platform utilizing CT images and clinical information for predicting radiotherapy locoregional recurrence in elderly esophageal cancer patients.

OBJECTIVE: To investigate the feasibility and accuracy of predicting locoregional recurrence (LR) in elderly patients with esophageal squamous cell ca...

New vision of HookEfficientNet deep neural network: Intelligent histopathological recognition system of non-small cell lung cancer.

BACKGROUND: Efficient and precise diagnosis of non-small cell lung cancer (NSCLC) is quite critical for subsequent targeted therapy and immunotherapy....

Integration of deep learning and habitat radiomics for predicting the response to immunotherapy in NSCLC patients.

BACKGROUND: The non-invasive biomarkers for predicting immunotherapy response are urgently needed to prevent both premature cessation of treatment and...

TumFlow: An AI Model for Predicting New Anticancer Molecules.

Melanoma is the fifth most common cancer in the United States. Conventional drug discovery methods are inherently time-consuming and costly, which imp...

A joint ESTRO and AAPM guideline for development, clinical validation and reporting of artificial intelligence models in radiation therapy.

BACKGROUND AND PURPOSE: Artificial Intelligence (AI) models in radiation therapy are being developed with increasing pace. Despite this, the radiation...

Identifying miRNA as biomarker for breast cancer subtyping using association rule.

- This paper presents a comprehensive study focused on breast cancer subtyping, utilizing a multifaceted approach that integrates feature selection, m...

GMILT: A Novel Transformer Network That Can Noninvasively Predict EGFR Mutation Status.

Noninvasively and accurately predicting the epidermal growth factor receptor (EGFR) mutation status is a clinically vital problem. Moreover, further i...

A Review of Nuclei Detection and Segmentation on Microscopy Images Using Deep Learning With Applications to Unbiased Stereology Counting.

The detection and segmentation of stained cells and nuclei are essential prerequisites for subsequent quantitative research for many diseases. Recentl...

A knowledge-enhanced interpretable network for early recurrence prediction of hepatocellular carcinoma via multi-phase CT imaging.

BACKGROUND: Predicting early recurrence (ER) of hepatocellular carcinoma (HCC) accurately can guide treatment decisions and further enhance survival. ...

Deep Learning for Histopathological Assessment of Esophageal Adenocarcinoma Precursor Lesions.

Histopathological assessment of esophageal biopsies is a key part in the management of patients with Barrett esophagus (BE) but prone to observer vari...

Machine learning-based model for predicting outcomes in cerebral hemorrhage patients with leukemia.

BACKGROUND AND PURPOSE: Intracranial hemorrhage (ICH) in leukemia patients progresses rapidly with high mortality. Limited data are available on imagi...

Integrated analysis of multiple transcriptomic approaches and machine learning integration algorithms reveals high endothelial venules as a prognostic immune-related biomarker in bladder cancer.

BACKGROUND: Despite the availability of established surgical and chemotherapy options, the treatment of bladder cancer (BCa) patients remains challeng...