Oncology/Hematology

Development and Validation of a Gene Signature Classifier for Consensus Molecular Subtyping of Colorectal Carcinoma in a CLIA-Certified Setting.

PURPOSE: Consensus molecular subtyping (CMS) of colorectal cancer has potential to reshape the colorectal cancer landscape. We developed and validated an assay that is applicable on formalin-fixed, paraffin-embedded (FFPE) samples of colorectal cancer and implemented the assay in a Clinical Laboratory Improvement Amendments (CLIA)-certified laboratory.

Artificial intelligence-based image classification methods for diagnosis of skin cancer: Challenges and opportunities.

Recently, there has been great interest in developing Artificial Intelligence (AI) enabled computer-aided diagnostics solutions for the diagnosis of skin cancer. With the increasing incidence of skin cancers, low awareness among a growing population, and a lack of adequate clinical expertise and services, there is an immediate need for AI systems to assist clinicians in this domain. A large number...

A Systematic Review of Machine Learning Techniques in Hematopoietic Stem Cell Transplantation (HSCT).

Machine learning techniques are widely used nowadays in the healthcare domain for the diagnosis, prognosis, and treatment of diseases. These technique...

Machine learning to predict the cancer-specific mortality of patients with primary non-metastatic invasive breast cancer.

PURPOSE: We used five machine-learning algorithms to predict cancer-specific mortality after surgical resection of primary non-metastatic invasive bre...

Advanced Imaging and Sampling in Barrett's Esophagus: Artificial Intelligence to the Rescue?

Because the current Barrett's esophagus (BE) surveillance protocol suffers from sampling error of random biopsies and a high miss-rate of early neopla...

Potential Candidates for Focal Therapy in Prostate Cancer in the Era of Magnetic Resonance Imaging-targeted Biopsy: A Large Multicenter Cohort Study.

BACKGROUND: Focal therapy (FT) with its favorable side-effect profile represents an option between active surveillance and traditional whole-gland tre...

Application of deep learning to predict advanced neoplasia using big clinical data in colorectal cancer screening of asymptomatic adults.

BACKGROUND/AIMS: We aimed to develop a deep learning model for the prediction of the risk of advanced colorectal neoplasia (ACRN) in asymptomatic adul...

Fully 3D Active Surface with Machine Learning for PET Image Segmentation.

In order to tackle three-dimensional tumor volume reconstruction from Positron Emission Tomography (PET) images, most of the existing algorithms rely ...

Tumor segmentation in automated whole breast ultrasound using bidirectional LSTM neural network and attention mechanism.

Accurate breast mass segmentation of automated breast ultrasound (ABUS) is a great help to breast cancer diagnosis and treatment. However, the lack of...

The prognostic role of end-of-treatment FDG-PET/CT in diffuse large B cell lymphoma: a pilot study application of neural networks to predict time-to-event.

PURPOSE: To evaluate the prognostic role of end-of-treatment (EoT) FDG-PET/CT parameters in diffuse large B cell lymphoma (DLBCL), and then to explore...

Dose prediction with deep learning for prostate cancer radiation therapy: Model adaptation to different treatment planning practices.

PURPOSE: This work aims to study the generalizability of a pre-developed deep learning (DL) dose prediction model for volumetric modulated arc therapy...

TNFPred: identifying tumor necrosis factors using hybrid features based on word embeddings.

BACKGROUND: Cytokines are a class of small proteins that act as chemical messengers and play a significant role in essential cellular processes includ...

Ensemble transfer learning for the prediction of anti-cancer drug response.

Transfer learning, which transfers patterns learned on a source dataset to a related target dataset for constructing prediction models, has been shown...

Development of a Malignancy Potential Binary Prediction Model Based on Deep Learning for the Mitotic Count of Local Primary Gastrointestinal Stromal Tumors.

OBJECTIVE: The mitotic count of gastrointestinal stromal tumors (GIST) is closely associated with the risk of planting and metastasis. The purpose of ...

Machine Learning Algorithms for the Prediction of Central Lymph Node Metastasis in Patients With Papillary Thyroid Cancer.

BACKGROUND: Central lymph node metastasis (CLNM) occurs frequently in patients with papillary thyroid cancer (PTC), but performing prophylactic centra...

Surgical technique for mesorectal division during robot-assisted laparoscopic tumor-specific mesorectal excision (TSME) for rectal cancer using da Vinci Si surgical system: the simple switching technique (SST).

In a narrow pelvic cavity, performing sufficient tumor-specific mesorectal excision (TSME) is difficult. Even in robot-assisted laparoscopic surgery (...

Artificial Intelligence Applied to Breast MRI for Improved Diagnosis.

Background Recognition of salient MRI morphologic and kinetic features of various malignant tumor subtypes and benign diseases, either visually or wit...

Machine learning-based prediction of microsatellite instability and high tumor mutation burden from contrast-enhanced computed tomography in endometrial cancers.

To evaluate whether radiomic features from contrast-enhanced computed tomography (CE-CT) can identify DNA mismatch repair deficient (MMR-D) and/or tum...

Image segmentation of plexiform neurofibromas from a deep neural network using multiple b-value diffusion data.

We assessed the accuracy of semi-automated tumor volume maps of plexiform neurofibroma (PN) generated by a deep neural network, compared to manual seg...

Deep learning for identifying corneal diseases from ocular surface slit-lamp photographs.

To demonstrate the identification of corneal diseases using a novel deep learning algorithm. A novel hierarchical deep learning network, which is comp...