Machine learning for grading and prognosis of esophageal dysplasia using mass spectrometry and histological imaging.

Background: Barrett's esophagus (BE) is a precursor lesion of esophageal adenocarcinoma and may progress from non-dysplastic through low-grade dysplasia (LGD) to high-grade dysplasia (HGD) and cancer. Grading BE is of crucial prognostic value and is currently based on the subjective evaluation of biopsies. This study aims to investigate the potential of machine learning (ML) using spatially resolved molecular data from mass spectrometry imaging (MSI) and histological data from microscopic hematoxylin and eosin (H&E)-stained imaging for computer-aided diagnosis and prognosis of BE.

Deep Learning-based Recurrence Prediction in Patients with Non-muscle-invasive Bladder Cancer.

Background: Non-muscle-invasive bladder cancer (NMIBC) is characterized by frequent recurrence of the disease, which is difficult to predict.

Objective: To combine digital histopathology slides with clinical data to predict 1- and 5-yr recurrence-free survival of NMIBC patients using deep learning.

Design, Setting, And Participants: Data of patients undergoing a transurethral resection of a bladder tumor between 2000 and 2018 at a Dutch academic medical center were selected.

Automated Detection and Grading of Non-Muscle-Invasive Urothelial Cell Carcinoma of the Bladder.

Accurate grading of non-muscle-invasive urothelial cell carcinoma is of major importance; however, high interobserver variability exists. A fully automated detection and grading network based on deep learning is proposed to enhance reproducibility. A total of 328 transurethral resection specimens from 232 patients were included, and a consensus reading by three specialized pathologists was used.

Toward Automated Bladder Tumor Stratification Using Confocal Laser Endomicroscopy.

Urothelial carcinoma of the bladder (UCB) is the most common urinary cancer. White-light cystoscopy (WLC) forms the corner stone for the diagnosis of UCB. However, histopathological assessment is required for adjuvant treatment selection.

Deep learning for automatic Gleason pattern classification for grade group determination of prostate biopsies.

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 biopsies, computer-aided grading becomes feasible. Computer-aided grading has the potential to improve histopathological grading and treatment selection for prostate cancer.

The First In Vivo Needle-Based Optical Coherence Tomography in Human Prostate: A Safety and Feasibility Study.

Objective: To demonstrate the safety and feasibility of clinical in vivo needle-based optical coherence tomography (OCT) imaging of the prostate.

Materials And Methods: Two patients with prostate cancer underwent each two percutaneous in vivo needle-based OCT measurements before transperineal template mapping biopsy. The OCT probe was introduced via a needle and positioned under ultrasound guidance.

Three-dimensional histopathological reconstruction of bladder tumours.

Background: Histopathological analysis is the cornerstone in bladder cancer (BCa) diagnosis. These analysis suffer from a moderate observer agreement in the staging of bladder cancer. Three-dimensional reconstructions have the potential to support the pathologists in visualizing spatial arrangements of structures, which may improve the interpretation of specimen.

Histopathology: ditch the slides, because digital and 3D are on show.

Due to the growing field of digital pathology, more and more digital histology slides are becoming available. This improves the accessibility, allows teleconsultations from specialized pathologists, improves education, and might give urologist the possibility to review the slides in patient management systems. Moreover, by stacking multiple two-dimensional (2D) digital slides, three-dimensional volumes can be created, allowing improved insight in the growth pattern of a tumor.

Characteristics of Misclassified CT Perfusion Ischemic Core in Patients with Acute Ischemic Stroke.

Background: CT perfusion (CTP) is used to estimate the extent of ischemic core and penumbra in patients with acute ischemic stroke. CTP reliability, however, is limited. This study aims to identify regions misclassified as ischemic core on CTP, using infarct on follow-up noncontrast CT.