Dataset Information

Radiologist-like artificial intelligence for grade group prediction of radical prostatectomy for reducing upgrading and downgrading from biopsy.

ABSTRACT: Rationale: To reduce upgrading and downgrading between needle biopsy (NB) and radical prostatectomy (RP) by predicting patient-level Gleason grade groups (GGs) of RP to avoid over- and under-treatment. Methods: In this study, we retrospectively enrolled 575 patients from two medical institutions. All patients received prebiopsy magnetic resonance (MR) examinations, and pathological evaluations of NB and RP were available. A total of 12,708 slices of original male pelvic MR images (T2-weighted sequences with fat suppression, T2WI-FS) containing 5405 slices of prostate tissue, and 2,753 tumor annotations (only T2WI-FS were annotated using RP pathological sections as ground truth) were analyzed for the prediction of patient-level RP GGs. We present a prostate cancer (PCa) framework, PCa-GGNet, that mimics radiologist behavior based on deep reinforcement learning (DRL). We developed and validated it using a multi-center format. Results: Accuracy (ACC) of our model outweighed NB results (0.815 [95% confidence interval (CI): 0.773-0.857] vs. 0.437 [95% CI: 0.335-0.539]). The PCa-GGNet scored higher (kappa value: 0.761) than NB (kappa value: 0.289). Our model significantly reduced the upgrading rate by 27.9% (P < 0.001) and downgrading rate by 6.4% (P = 0.029). Conclusions: DRL using MRI can be applied to the prediction of patient-level RP GGs to reduce upgrading and downgrading from biopsy, potentially improving the clinical benefits of prostate cancer oncologic controls.

SUBMITTER: Shao L

PROVIDER: S-EPMC7481433 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Radiologist-like artificial intelligence for grade group prediction of radical prostatectomy for reducing upgrading and downgrading from biopsy.

Shao Lizhi L Yan Ye Y Liu Zhenyu Z Ye Xiongjun X Xia Haizhui H Zhu Xuehua X Zhang Yuting Y Zhang Zhiying Z Chen Huiying H He Wei W Liu Cheng C Lu Min M Huang Yi Y Ma Lulin L Sun Kai K Zhou Xuezhi X Yang Guanyu G Lu Jian J Tian Jie J

Theranostics 20200902 22

<b>Rationale:</b> To reduce upgrading and downgrading between needle biopsy (NB) and radical prostatectomy (RP) by predicting patient-level Gleason grade groups (GGs) of RP to avoid over- and under-treatment. <b>Methods:</b> In this study, we retrospectively enrolled 575 patients from two medical institutions. All patients received prebiopsy magnetic resonance (MR) examinations, and pathological evaluations of NB and RP were available. A total of 12,708 slices of original male pelvic MR images ( ...[more]

PMID: 32929343

Similar Datasets

Project description:ImportanceMammography screening currently relies on subjective human interpretation. Artificial intelligence (AI) advances could be used to increase mammography screening accuracy by reducing missed cancers and false positives.ObjectiveTo evaluate whether AI can overcome human mammography interpretation limitations with a rigorous, unbiased evaluation of machine learning algorithms.Design, setting, and participantsIn this diagnostic accuracy study conducted between September 2016 and November 2017, an international, crowdsourced challenge was hosted to foster AI algorithm development focused on interpreting screening mammography. More than 1100 participants comprising 126 teams from 44 countries participated. Analysis began November 18, 2016.Main outcomes and measurementsAlgorithms used images alone (challenge 1) or combined images, previous examinations (if available), and clinical and demographic risk factor data (challenge 2) and output a score that translated to cancer yes/no within 12 months. Algorithm accuracy for breast cancer detection was evaluated using area under the curve and algorithm specificity compared with radiologists' specificity with radiologists' sensitivity set at 85.9% (United States) and 83.9% (Sweden). An ensemble method aggregating top-performing AI algorithms and radiologists' recall assessment was developed and evaluated.ResultsOverall, 144 231 screening mammograms from 85 580 US women (952 cancer positive ≤12 months from screening) were used for algorithm training and validation. A second independent validation cohort included 166 578 examinations from 68 008 Swedish women (780 cancer positive). The top-performing algorithm achieved an area under the curve of 0.858 (United States) and 0.903 (Sweden) and 66.2% (United States) and 81.2% (Sweden) specificity at the radiologists' sensitivity, lower than community-practice radiologists' specificity of 90.5% (United States) and 98.5% (Sweden). Combining top-performing algorithms and US radiologist assessments resulted in a higher area under the curve of 0.942 and achieved a significantly improved specificity (92.0%) at the same sensitivity.Conclusions and relevanceWhile no single AI algorithm outperformed radiologists, an ensemble of AI algorithms combined with radiologist assessment in a single-reader screening environment improved overall accuracy. This study underscores the potential of using machine learning methods for enhancing mammography screening interpretation.

Project description:Intraoperative optical biopsy technologies may aid in the identification of important anatomical landmarks and improve surgical outcomes of robotic assisted radical prostatectomy. We evaluate the feasibility of confocal laser endomicroscopy during robotic assisted radical prostatectomy.A total of 21 patients with biopsy proven prostate cancer scheduled for robotic assisted radical prostatectomy were recruited. After intravenous administration of fluorescein 15 patients underwent in vivo intraoperative confocal laser endomicroscopy of prostatic and periprostatic structures using a 2.6 or 0.85 mm imaging probe. Standard robotic instruments were used to grasp and maneuver the confocal laser endomicroscopy probes for image acquisition. Confocal laser endomicroscopy imaging was performed ex vivo on fresh prostate specimens from 20 patients. Confocal video sequences acquired in vivo and ex vivo were reviewed and analyzed, with additional image processing using a mosaicing algorithm. Processed confocal images were compared with standard hematoxylin and eosin analysis of imaged regions.Confocal laser endomicroscopy was successfully integrated with robotic surgery, including co-registration of confocal video sequences with white light and probe handling with standard robotic instrumentation. Intraoperative confocal laser endomicroscopy imaging of the neurovascular bundle before and after nerve sparing dissection revealed characteristic features including dynamic vascular flow and intact axon fibers. Ex vivo confocal imaging of the prostatic parenchyma demonstrated normal prostate glands, stroma and prostatic carcinoma.We report the initial feasibility of optical biopsy of prostatic and periprostatic tissue during robotic assisted radical prostatectomy. Image guidance and tissue interrogation using confocal laser endomicroscopy offer a new intraoperative imaging method that has the potential to improve the functional and oncologic outcomes of prostate cancer surgery.

Dataset Information

Radiologist-like artificial intelligence for grade group prediction of radical prostatectomy for reducing upgrading and downgrading from biopsy.

Publications

Radiologist-like artificial intelligence for grade group prediction of radical prostatectomy for reducing upgrading and downgrading from biopsy.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets