Oncology/Hematology

Applications of Artificial Intelligence in Musculoskeletal Imaging: From the Request to the Report.

Artificial intelligence (AI) will transform every step in the imaging value chain, including interpretive and noninterpretive components. Radiologists should familiarize themselves with AI developments to become leaders in their clinical implementation. This article explores the impact of AI through the entire imaging cycle of musculoskeletal radiology, from the placement of the requisition to the...

Using Deep Learning with Convolutional Neural Network Approach to Identify the Invasion Depth of Endometrial Cancer in Myometrium Using MR Images: A Pilot Study.

Myometrial invasion affects the prognosis of endometrial cancer. However, discrepancies exist between pre-operative magnetic resonance imaging staging and post-operative pathological staging. This study aims to validate the accuracy of artificial intelligence (AI) for detecting the depth of myometrial invasion using a deep learning technique on magnetic resonance images. We obtained 4896 contrast-...

Deep computational pathology in breast cancer.

Deep Learning (DL) algorithms are a set of techniques that exploit large and/or complex real-world datasets for cross-domain and cross-discipline pred...

Artificial intelligence-assisted cytology for detection of cervical intraepithelial neoplasia or invasive cancer: A multicenter, clinical-based, observational study.

OBJECTIVE: Artificial intelligence (AI) could automatedly detect abnormalities in digital cytological images, however, the effect in cervical cancer s...

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model.

Machine learning (ML) algorithms permit the integration of different features into a model to perform classification or regression tasks with an accur...

Quantifying the Brain Metastatic Tumor Micro-Environment using an Organ-On-A Chip 3D Model, Machine Learning, and Confocal Tomography.

Brain metastases are the most lethal cancer lesions; 10-30% of all cancers metastasize to the brain, with a median survival of only ~5-20 months, depe...

MRI radiomics for the prediction of recurrence in patients with clinically non-functioning pituitary macroadenomas.

Twelve to 66% of patients with clinically non-functioning pituitary adenoma (NFPA) experience tumor recurrence 1-5 years after the first surgery. Neve...

Identification of intraductal carcinoma of the prostate on tissue specimens using Raman micro-spectroscopy: A diagnostic accuracy case-control study with multicohort validation.

BACKGROUND: Prostate cancer (PC) is the most frequently diagnosed cancer in North American men. Pathologists are in critical need of accurate biomarke...

Bone metastasis classification using whole body images from prostate cancer patients based on convolutional neural networks application.

Bone metastasis is one of the most frequent diseases in prostate cancer; scintigraphy imaging is particularly important for the clinical diagnosis of ...

The deep learning model combining CT image and clinicopathological information for predicting ALK fusion status and response to ALK-TKI therapy in non-small cell lung cancer patients.

PURPOSE: This study aimed to investigate the deep learning model (DLM) combining computed tomography (CT) images and clinicopathological information f...

Deep learning-based survival analysis for brain metastasis patients with the national cancer database.

PURPOSE: Prognostic indices such as the Brain Metastasis Graded Prognostic Assessment have been used in clinical settings to aid physicians and patien...

Deep Learning for Pediatric Posterior Fossa Tumor Detection and Classification: A Multi-Institutional Study.

BACKGROUND AND PURPOSE: Posterior fossa tumors are the most common pediatric brain tumors. MR imaging is key to tumor detection, diagnosis, and therap...

Time-series cardiovascular risk factors and receipt of screening for breast, cervical, and colon cancer: The Guideline Advantage.

BACKGROUND: Cancer is the second leading cause of death in the United States. Cancer screenings can detect precancerous cells and allow for earlier di...

New convolutional neural network model for screening and diagnosis of mammograms.

Breast cancer is the most common cancer in women and poses a great threat to women's life and health. Mammography is an effective method for the diagn...

Classification of cervical neoplasms on colposcopic photography using deep learning.

Colposcopy is widely used to detect cervical cancers, but experienced physicians who are needed for an accurate diagnosis are lacking in developing co...

A Linear Regression and Deep Learning Approach for Detecting Reliable Genetic Alterations in Cancer Using DNA Methylation and Gene Expression Data.

DNA methylation change has been useful for cancer biomarker discovery, classification, and potential treatment development. So far, existing methods u...

Artificial neural network model for preoperative prediction of severe liver failure after hemihepatectomy in patients with hepatocellular carcinoma.

BACKGROUND: Posthepatectomy liver failure is a worrisome complication after major hepatectomy for hepatocellular carcinoma and is the leading cause of...

Real-time assessment of video images for esophageal squamous cell carcinoma invasion depth using artificial intelligence.

BACKGROUND: Although optimal treatment of superficial esophageal squamous cell carcinoma (SCC) requires accurate evaluation of cancer invasion depth, ...

MRI-Based Deep-Learning Model for Distant Metastasis-Free Survival in Locoregionally Advanced Nasopharyngeal Carcinoma.

BACKGROUND: Distant metastasis is the primary cause of treatment failure in locoregionally advanced nasopharyngeal carcinoma (LANPC).

Rule-based automatic diagnosis of thyroid nodules from intraoperative frozen sections using deep learning.

Frozen sections provide a basis for rapid intraoperative diagnosis that can guide surgery, but the diagnoses often challenge pathologists. Here we pro...