Performance of MR fusion biopsy, systematic biopsy and combined biopsy on prostate cancer detection rate in 1229 patients stratified by PI-RADSv2 score on 3T multi-parametric MRI.

Purpose: We analyzed the additional value of systematic biopsy (SB) to MR-Ultrasound fusion biopsy (MRgFbx) for detection of clinically significant prostate cancer (csPCa), as increased sampling may cause increased morbidity.

Materials And Methods: This retrospective study cohort was comprised of 1229 biopsy sessions between July 2016 and May 2020 in men who had a Prostate Imaging-Reporting and Data System (PI-RADSv2) category ≥ 3 lesion on 3 Tesla multiparametric MRI (3TmpMRI) and subsequent combined biopsy (CB; MRgFbx and SB) for suspected prostate cancer (PCa). Cancer detection rates (CDR) were calculated for CB, MRgFbx and SB in the study cohort and sub-cohorts stratified by biopsy history and PI-RADSv2 category.

Federated Learning with Research Prototypes: Application to Multi-Center MRI-based Detection of Prostate Cancer with Diverse Histopathology.

Rationale And Objectives: Early prostate cancer detection and staging from MRI is extremely challenging for both radiologists and deep learning algorithms, but the potential to learn from large and diverse datasets remains a promising avenue to increase their performance within and across institutions. To enable this for prototype-stage algorithms, where the majority of existing research remains, we introduce a flexible federated learning framework for cross-site training, validation, and evaluation of custom deep learning prostate cancer detection algorithms.

Materials And Methods: We introduce an abstraction of prostate cancer groundtruth that represents diverse annotation and histopathology data.

Detecting, extracting, and mapping of inland surface water using Landsat 8 Operational Land Imager: A case study of Pune district, India.

Recent developments in optical satellite remote sensing have led to a new era in the detection of surface water with its changing dynamics. This study presents the creation of surface water inventory for a part of Pune district (an administrative area), in India using the Landsat 8 Operational Land Imager (OLI) and a multi spectral water indices method. A total of 13 Landsat 8 OLI cloud free images were analyzed for surface water detection.

Federated learning improves site performance in multicenter deep learning without data sharing.

Objective: To demonstrate enabling multi-institutional training without centralizing or sharing the underlying physical data via federated learning (FL).

Materials And Methods: Deep learning models were trained at each participating institution using local clinical data, and an additional model was trained using FL across all of the institutions.

Results: We found that the FL model exhibited superior performance and generalizability to the models trained at single institutions, with an overall performance level that was significantly better than that of any of the institutional models alone when evaluated on held-out test sets from each institution and an outside challenge dataset.