The development of decision support systems for pathology and their deployment in clinical practice have been hindered by the need for large manually annotated datasets. To overcome this problem, we present a multiple instance learning-based deep lea...
OBJECTIVE: Stereotactic radiosurgery (SRS) has been increasingly applied for malignant meningiomas as an alternative to conventionally fractioned radiation therapy. We performed a retrospective analysis of an institutional patient cohort with maligna...
AJR. American journal of roentgenology
Jul 3, 2019
The objective of our study was to analyze the feasibility and potential role of robotic-assisted transrectal MRI-guided biopsy for the diagnosis of prostate cancer. A total of 57 patients (mean age, 67 ± 6 [SD] years; age range, 57-83 years; mean p...
This study aims to introduce as proof of concept a combination model for classification of prostate cancer using deep learning approaches. We utilized patients with prostate cancer who underwent surgical treatment representing the various conditions ...
OBJECTIVE: The purpose of this study was: To test whether machine learning classifiers for transition zone (TZ) and peripheral zone (PZ) can correctly classify prostate tumors into those with/without a Gleason 4 component, and to compare the performa...
PURPOSE: Recent advances in machine learning have enabled better understanding of large and complex visual data. Here, we aim to investigate patient outcome prediction with a machine learning method using only an image of tumour sample as an input.
Virchows Archiv : an international journal of pathology
May 16, 2019
Histopathologic grading of prostate cancer using Gleason patterns (GPs) is subject to a large inter-observer variability, which may result in suboptimal treatment of patients. With the introduction of digitization and whole-slide images of prostate b...
OBJECTIVES: To investigate the association between proton magnetic resonance spectroscopy (H-MRS) metabolic features and the grade of gliomas, and to establish a machine-learning model to predict the glioma grade.
Clinical cancer research : an official journal of the American Association for Cancer Research
Apr 11, 2019
PURPOSE: We aimed to develop an ovarian cancer-specific predictive framework for clinical stage, histotype, residual tumor burden, and prognosis using machine learning methods based on multiple biomarkers.