Oncology/Hematology

The State of Robotic Partial Nephrectomy: Operative, Functional, and Oncological Outcomes From A Robust Multi-Institution Collaborative.

OBJECTIVE: To describe the most recent surgical, functional, and oncological outcomes of RPN utilizing one of the largest, prospectively maintained, multi-institution consortium of patients undergoing robotic renal surgery.

Comparison of image quality and pancreatic ductal adenocarcinoma conspicuity between the low-kVp and dual-energy CT reconstructed with deep-learning image reconstruction algorithm.

PURPOSE: To compare the image quality and conspicuity of pancreatic ductal adenocarcinoma (PDAC) between the low-kVp and dual-energy pancreatic protocol CT reconstructed with deep-learning image reconstruction (DLIR).

Application of machine learning techniques for predicting survival in ovarian cancer.

BACKGROUND: Ovarian cancer is the fifth leading cause of mortality among women in the United States. Ovarian cancer is also known as forgotten cancer ...

A Deep Learning Workflow for Mass-Forming Intrahepatic Cholangiocarcinoma and Hepatocellular Carcinoma Classification Based on MRI.

OBJECTIVE: Precise classification of mass-forming intrahepatic cholangiocarcinoma (MF-ICC) and hepatocellular carcinoma (HCC) based on magnetic resona...

Raman microspectroscopy and machine learning for use in identifying radiation-induced lung toxicity.

OBJECTIVE: In this work, we explore and develop a method that uses Raman spectroscopy to measure and differentiate radiation induced toxicity in murin...

Deep learning assisted contrast-enhanced CT-based diagnosis of cervical lymph node metastasis of oral cancer: a retrospective study of 1466 cases.

OBJECTIVES: Lymph node (LN) metastasis is a common cause of recurrence in oral cancer; however, the accuracy of distinguishing positive and negative L...

Low-contrast-dose liver CT using low monoenergetic images with deep learning-based denoising for assessing hepatocellular carcinoma: a randomized controlled noninferiority trial.

OBJECTIVE: Low monoenergetic images obtained using noise-reduction techniques may reduce CT contrast media requirements. We aimed to investigate the e...

Feasibility study of deep learning-based markerless real-time lung tumor tracking with orthogonal X-ray projection images.

PURPOSE: The feasibility of a deep learning-based markerless real-time tumor tracking (RTTT) method was retrospectively studied with orthogonal kV X-r...

Deep learning as a new tool in the diagnosis of mycosis fungoides.

Mycosis Fungoides (MF) makes up the most of the cutaneous lymphomas. As a malignant disease, the greatest diagnostical challenge is to timely differen...

An accurate prediction of the origin for bone metastatic cancer using deep learning on digital pathological images.

BACKGROUND: Determining the origin of bone metastatic cancer (OBMC) is of great significance to clinical therapeutics. It is challenging for pathologi...

The Development of Symbolic Expressions for Fire Detection with Symbolic Classifier Using Sensor Fusion Data.

Fire is usually detected with fire detection systems that are used to sense one or more products resulting from the fire such as smoke, heat, infrared...

Contrast-Enhanced Ultrasound with Deep Learning with Attention Mechanisms for Predicting Microvascular Invasion in Single Hepatocellular Carcinoma.

RATIONALE AND OBJECTIVES: Prediction of microvascular invasion (MVI) status of hepatocellular carcinoma (HCC) holds clinical significance for decision...

Development and validation of a machine learning algorithm for predicting diffuse midline glioma, H3 K27-altered, H3 K27 wild-type high-grade glioma, and primary CNS lymphoma of the brain midline in adults.

OBJECTIVE: Preoperative diagnosis of diffuse midline glioma, H3 K27-altered (DMG-A) and midline high-grade glioma without H3 K27 alteration (DMG-W), a...

Mesoscopic description of hippocampal replay and metastability in spiking neural networks with short-term plasticity.

Bottom-up models of functionally relevant patterns of neural activity provide an explicit link between neuronal dynamics and computation. A prime exam...

Deep Learning-Based Objective and Reproducible Osteosarcoma Chemotherapy Response Assessment and Outcome Prediction.

Osteosarcoma is the most common primary bone cancer, whose standard treatment includes pre-operative chemotherapy followed by resection. Chemotherapy ...

Detection of Colorectal Adenocarcinoma and Grading Dysplasia on Histopathologic Slides Using Deep Learning.

Colorectal cancer (CRC) is one of the most common types of cancer among men and women. The grading of dysplasia and the detection of adenocarcinoma ar...

Deep learning-based assessment of body composition and liver tumour burden for survival modelling in advanced colorectal cancer.

BACKGROUND: Personalized therapy planning remains a significant challenge in advanced colorectal cancer care, despite extensive research on prognostic...

Ensemble learning based assessment of the role of transcription factors in gene expression.

Cancer cells are formed when the associated, active genes fail to function the way they are meant to function. Multiple genes collectively control cel...

A clinical and time savings evaluation of a deep learning automatic contouring algorithm.

Automatic contouring algorithms may streamline clinical workflows by reducing normal organ-at-risk (OAR) contouring time. Here we report the first com...

Predicting Underestimation of Invasive Cancer in Patients with Core-Needle-Biopsy-Diagnosed Ductal Carcinoma In Situ Using Deep Learning Algorithms.

The prediction of an occult invasive component in ductal carcinoma in situ (DCIS) before surgery is of clinical importance because the treatment strat...