Oncology/Hematology

AF-SENet: Classification of Cancer in Cervical Tissue Pathological Images Based on Fusing Deep Convolution Features.

Cervical cancer is the fourth most common cancer in the world. Whole-slide images (WSIs) are an important standard for the diagnosis of cervical cancer. Missed diagnoses and misdiagnoses often occur due to the high similarity in pathological cervical images, the large number of readings, the long reading time, and the insufficient experience levels of pathologists. Existing models have insufficien...

Machine learning and natural language processing (NLP) approach to predict early progression to first-line treatment in real-world hormone receptor-positive (HR+)/HER2-negative advanced breast cancer patients.

BACKGROUND: CDK4/6 inhibitors plus endocrine therapies are the current standard of care in the first-line treatment of HR+/HER2-negative metastatic breast cancer, but there are no well-established clinical or molecular predictive factors for patient response. In the era of personalised oncology, new approaches for developing predictive models of response are needed.

Diagnostic Value of Pyruvate Kinase Isoenzyme Type M2 in Colon Cancer Proven with Colonoscopy.

BACKGROUND: Colonoscopy is the gold standard for colon cancer screening; it is also associated with a high cost and complication. Proliferating cells,...

Artificial neural networks for simultaneously predicting the risk of multiple co-occurring symptoms among patients with cancer.

Patients with cancer often exhibit multiple co-occurring symptoms which can impact the type of treatment received, recovery, and long-term health. We ...

Segmentation and classification of thyroid follicular neoplasm using cascaded convolutional neural network.

In this paper, we present a segmentation and classification method for thyroid follicular neoplasms based on a combination of the prior-based level se...

Data-driven dose calculation algorithm based on deep U-Net.

Accurate and efficient dose calculation is an important prerequisite to ensure the success of radiation therapy. However, all the dose calculation alg...

Interactive Classification of Whole-Slide Imaging Data for Cancer Researchers.

Whole-slide histology images contain information that is valuable for clinical and basic science investigations of cancer but extracting quantitative ...

Robotic Partial Left Hepatectomy for Metastatic Colon Cancer to the Liver. Application of Minimally Invasive Technique in Cancer Surgery.

Salvage Robot-Assisted Minimally Invasive Esophagectomy (RAMIE) for T4b Esophageal Cancer After Definitive Chemoradiotherapy.

BACKGROUND: Patients  with esophageal cancer  that invades adjacent structures (cT4b) are precluded from surgery and usually treated with definitive c...

Feasibility of automated planning for whole-brain radiation therapy using deep learning.

PURPOSE: The purpose of this study was to develop automated planning for whole-brain radiation therapy (WBRT) using a U-net-based deep-learning model ...

Three-Dimensional Vessel Segmentation in Whole-Tissue and Whole-Block Imaging Using a Deep Neural Network: Proof-of-Concept Study.

In the field of pathology, micro-computed tomography (micro-CT) has become an attractive imaging modality because it enables full analysis of the thre...

Imaging Beyond Seeing: Early Prognosis of Cancer Treatment.

Assessing cancer response to therapeutic interventions has been realized as an important course to early predict curative efficacy and treatment outco...

Colorectal polyp characterization with endocytoscopy: Ready for widespread implementation with artificial intelligence?

Endocytoscopy provides an in-vivo visualization of nuclei and micro-vessels at the cellular level in real-time, facilitating so-called "optical biopsy...

A physics-guided modular deep-learning based automated framework for tumor segmentation in PET.

An important need exists for reliable positron emission tomography (PET) tumor-segmentation methods for tasks such as PET-based radiation-therapy plan...

Deep learning-based real-time volumetric imaging for lung stereotactic body radiation therapy: a proof of concept study.

Due to the inter- and intra- variation of respiratory motion, it is highly desired to provide real-time volumetric images during the treatment deliver...

Denoising Autoencoder, A Deep Learning Algorithm, Aids the Identification of A Novel Molecular Signature of Lung Adenocarcinoma.

Precise biomarker development is a key step in disease management. However, most of the published biomarkers were derived from a relatively small numb...

Necrotic enteritis challenge regulates peroxisome proliferator-1 activated receptors signaling and β-oxidation pathways in broiler chickens.

Necrotic enteritis (NE) is an important enteric disease in poultry and has become a major concern in poultry production in the post-antibiotic era. Th...

ProsRegNet: A deep learning framework for registration of MRI and histopathology images of the prostate.

Magnetic resonance imaging (MRI) is an increasingly important tool for the diagnosis and treatment of prostate cancer. However, interpretation of MRI ...

Laparoscopic cryoablation for small renal masses: Oncological outcomes at 5-year follow-up.

: To evaluate the oncological outcome at 5-year follow-up after laparoscopic cryoablation (LCA) for small renal masses (SRMs), as there is an increasi...

Web-based interactive mapping from data dictionaries to ontologies, with an application to cancer registry.

BACKGROUND: The Kentucky Cancer Registry (KCR) is a central cancer registry for the state of Kentucky that receives data about incident cancer cases f...