Oncology/Hematology

Two-stage deep learning model for fully automated pancreas segmentation on computed tomography: Comparison with intra-reader and inter-reader reliability at full and reduced radiation dose on an external dataset.

PURPOSE: To develop a two-stage three-dimensional (3D) convolutional neural networks (CNNs) for fully automated volumetric segmentation of pancreas on computed tomography (CT) and to further evaluate its performance in the context of intra-reader and inter-reader reliability at full dose and reduced radiation dose CTs on a public dataset.

Robotic D2 Total Gastrectomy with Fluorescent Lymphatic Mapping for Gastric Cancer: Effective Use of the 4th Arm.

Minimally invasive surgery techniques have evolved remarkably over the past few decades in the field of surgical oncology, including robotic techniques for gastric malignancies. Bedside surgical assistance is often limited by operating table space or surgeon availability. Operating surgeons need to understand such limitations in robotic surgery assistance and how to maximize the effective use of t...

Artificial neural network model to predict post-hepatectomy early recurrence of hepatocellular carcinoma without macroscopic vascular invasion.

BACKGROUND: The accurate prediction of post-hepatectomy early recurrence (PHER) of hepatocellular carcinoma (HCC) is vital in determining postoperativ...

CycleGAN for interpretable online EMT compensation.

PURPOSE: Electromagnetic tracking (EMT) can partially replace X-ray guidance in minimally invasive procedures, reducing radiation in the OR. However, ...

Tumor attention networks: Better feature selection, better tumor segmentation.

Compared with the traditional analysis of computed tomography scans, automatic liver tumor segmentation can supply precise tumor volumes and reduce th...

Expanding TNM for lung cancer through machine learning.

BACKGROUND: Expanding the tumor, lymph node, metastasis (TNM) staging system by accommodating new prognostic and predictive factors for cancer will im...

Deep learning-based tumor microenvironment analysis in colon adenocarcinoma histopathological whole-slide images.

BACKGROUND AND OBJECTIVE: Colon cancer is a fatal disease, and a comprehensive understanding of the tumor microenvironment (TME) could lead to better ...

Machine learning for the prediction of bone metastasis in patients with newly diagnosed thyroid cancer.

OBJECTIVES: This study aimed to establish a machine learning prediction model that can be used to predict bone metastasis (BM) in patients with newly ...

Predicting gastric cancer outcome from resected lymph node histopathology images using deep learning.

N-staging is a determining factor for prognostic assessment and decision-making for stage-based cancer therapeutic strategies. Visual inspection of wh...

Deep Learning for Automatic Differential Diagnosis of Primary Central Nervous System Lymphoma and Glioblastoma: Multi-Parametric Magnetic Resonance Imaging Based Convolutional Neural Network Model.

BACKGROUND: Differential diagnosis of primary central nervous system lymphoma (PCNSL) and glioblastoma (GBM) is useful to guide treatment strategies.

An imageomics and multi-network based deep learning model for risk assessment of liver transplantation for hepatocellular cancer.

INTRODUCTION: Liver transplantation (LT) is an effective treatment for hepatocellular carcinoma (HCC), the most common type of primary liver cancer. P...

A Systematic Approach for MRI Brain Tumor Localization and Segmentation Using Deep Learning and Active Contouring.

One of the main requirements of tumor extraction is the annotation and segmentation of tumor boundaries correctly. For this purpose, we present a thre...

Convolutional neural network for classifying primary liver cancer based on triple-phase CT and tumor marker information: a pilot study.

PURPOSE: To develop convolutional neural network (CNN) models for differentiating intrahepatic cholangiocarcinoma (ICC) from hepatocellular carcinoma ...

A new rapid diagnostic system with ambient mass spectrometry and machine learning for colorectal liver metastasis.

BACKGROUND: Probe electrospray ionization-mass spectrometry (PESI-MS) can rapidly visualize mass spectra of small, surgically obtained tissue samples,...

Gated Graph Attention Network for Cancer Prediction.

With its increasing incidence, cancer has become one of the main causes of worldwide mortality. In this work, we mainly propose a novel attention-base...

Use of deep learning for detection, characterisation and prediction of metastatic disease from computerised tomography: a systematic review.

CT is widely used for diagnosis, staging and management of cancer. The presence of metastasis has significant implications on treatment and prognosis....

Oncological outcomes of dose reductions in cisplatin due to renal dysfunction for patients with metastatic urothelial carcinoma.

OBJECTIVE: To investigate whether dose reductions in cisplatin due to renal dysfunction were associated with worse clinical outcomes in metastatic uro...

Generative Adversarial Networks to Synthesize Missing T1 and FLAIR MRI Sequences for Use in a Multisequence Brain Tumor Segmentation Model.

Background Missing MRI sequences represent an obstacle in the development and use of deep learning (DL) models that require multiple inputs. Purpose T...

Deep learning classification of lung cancer histology using CT images.

Tumor histology is an important predictor of therapeutic response and outcomes in lung cancer. Tissue sampling for pathologist review is the most reli...

A robust electrochemical immunosensor based on core-shell nanostructured silica-coated silver for cancer (carcinoembryonic-antigen-CEA) diagnosis.

This work addresses the fabrication of an efficient, novel, and economically viable immunosensing armamentarium that will detect the carcinoembryonic ...