Artificial intelligence‐based versus manual assessment of prostate cancer in the prostate gland: a method comparison study

Mortensen, Mike Allan; Borrelli, Pablo; Poulsen, Mads Hvid; Gerke, Oke; Enqvist, Olof; Ulén, Johannes; Trägårdh, Elin; Constantinescu, Caius Mihail; Edenbrandt, Lars; Lund, Lars; Høilund‐Carlsen, Poul Flemming

doi:10.1111/cpf.12592

Cited by 31 publications

(20 citation statements)

References 28 publications

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“…Mortensen et al [ 9 ] and Polymeri et al [ 10 ] used automated segmentation of the prostate. A CNN was trained for automated measurements in [18F]-choline PET/CT scans obtained before radical prostatectomy in patients with newly diagnosed prostate cancer [ 9 ]. Automated standardised uptake values from the PET images were obtained for the prostate.…”

Section: Discussionmentioning

confidence: 99%

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RECOMIA—a cloud-based platform for artificial intelligence research in nuclear medicine and radiology

et al. 2020

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Background: Artificial intelligence (AI) is about to transform medical imaging. The Research Consortium for Medical Image Analysis (RECOMIA), a not-for-profit organisation, has developed an online platform to facilitate collaboration between medical researchers and AI researchers. The aim is to minimise the time and effort researchers need to spend on technical aspects, such as transfer, display, and annotation of images, as well as legal aspects, such as de-identification. The purpose of this article is to present the RECOMIA platform and its AI-based tools for organ segmentation in computed tomography (CT), which can be used for extraction of standardised uptake values from the corresponding positron emission tomography (PET) image. Results: The RECOMIA platform includes modules for (1) local de-identification of medical images, (2) secure transfer of images to the cloud-based platform, (3) display functions available using a standard web browser, (4) tools for manual annotation of organs or pathology in the images, (5) deep learning-based tools for organ segmentation or other customised analyses, (6) tools for quantification of segmented volumes, and (7) an export function for the quantitative results. The AI-based tool for organ segmentation in CT currently handles 100 organs (77 bones and 23 soft tissue organs). The segmentation is based on two convolutional neural networks (CNNs): one network to handle organs with multiple similar instances, such as vertebrae and ribs, and one network for all other organs. The CNNs have been trained using CT studies from 339 patients. Experienced radiologists annotated organs in the CT studies. The performance of the segmentation tool, measured as mean Dice index on a manually annotated test set, with 10 representative organs, was 0.93 for all foreground voxels, and the mean Dice index over the organs were 0.86 (0.82 for the soft tissue organs and 0.90 for the bones). Conclusion: The paper presents a platform that provides deep learning-based tools that can perform basic organ segmentations in CT, which can then be used to automatically obtain the different measurement in the corresponding PET image. The RECOMIA platform is available on request at www.recomia.org for research purposes.

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Section: Discussionmentioning

confidence: 99%

“…The CNN-based organ segmentation in CT studies in RECOMIA has been used in multiple studies [7][8][9][10][11][12]. These studies were approved by the Regional Ethical Review Board (#295/08) and were performed following the Declaration of Helsinki.…”

Section: Patients and Manual Segmentationsmentioning

confidence: 99%

RECOMIA—a cloud-based platform for artificial intelligence research in nuclear medicine and radiology

et al. 2020

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“…(2018) at the Skåne University Hospital in Sweden between February 2008 and November 2011; 50 patients from a study by Mortensen et al. (2019) at the Odense University Hospital in Denmark between January 2013 and April 2014. The patients were randomly divided into a training group (319 patients; 80%) and a test group (80 patients; 20%).…”

Section: Methodsmentioning

confidence: 99%

Artificial intelligence‐based detection of lymph node metastases by PET/CT predicts prostate cancer‐specific survival

Borrelli

Larsson

Ulén³

et al. 2020

Clin Physio Funct Imaging

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Introduction Lymph node metastases are a key prognostic factor in prostate cancer (PCa), but detecting lymph node lesions from PET/CT images is a subjective process resulting in inter‐reader variability. Artificial intelligence (AI)‐based methods can provide an objective image analysis. We aimed at developing and validating an AI‐based tool for detection of lymph node lesions. Methods A group of 399 patients with biopsy‐proven PCa who had undergone 18F‐choline PET/CT for staging prior to treatment were used to train (n = 319) and test (n = 80) the AI‐based tool. The tool consisted of convolutional neural networks using complete PET/CT scans as inputs. In the test set, the AI‐based lymph node detections were compared to those of two independent readers. The association with PCa‐specific survival was investigated. Results The AI‐based tool detected more lymph node lesions than Reader B (98 vs. 87/117; p = .045) using Reader A as reference. AI‐based tool and Reader A showed similar performance (90 vs. 87/111; p = .63) using Reader B as reference. The number of lymph node lesions detected by the AI‐based tool, PSA, and curative treatment was significantly associated with PCa‐specific survival. Conclusion This study shows the feasibility of using an AI‐based tool for automated and objective interpretation of PET/CT images that can provide assessments of lymph node lesions comparable with that of experienced readers and prognostic information in PCa patients.

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“…With the development of ML and new molecular probes, DNN has been used in 68 Ga-PSMA imaging and made a better performance in the diagnosis of bone metastasis and lymph node metastasis from prostate cancer (112,113). Automated PET/CT segmentation trained with CNN was also used in the prostate cancer lesion uptake which was in association with overall survival (114,115). While PET/CT performs a whole body scan at one time, it can accurately detect systemic diseases, such as leukemia, lymphoma, multiple myeloma, etc.…”

Section: Application Of ML In Petmentioning

confidence: 99%

A review of the application of machine learning in molecular imaging

Yin¹,

Cao²,

Wang³

et al. 2021

Ann Transl Med

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Molecular imaging (MI) is a science that uses imaging methods to reflect the changes of molecular level in living state and conduct qualitative and quantitative studies on its biological behaviors in imaging. Optical molecular imaging (OMI) and nuclear medical imaging are two key research fields of MI.OMI technology refers to the optical information generated by the imaging target (such as tumors) due to drug intervention and other reasons. By collecting the optical information, researchers can track the motion trajectory of the imaging target at the molecular level. Owing to its high specificity and sensitivity, OMI has been widely used in preclinical research and clinical surgery. Nuclear medical imaging mainly detects ionizing radiation emitted by radioactive substances. It can provide molecular information for early diagnosis, effective treatment and basic research of diseases, which has become one of the frontiers and hot topics in the field of medicine in the world today. Both OMI and nuclear medical imaging technology require a lot of data processing and analysis. In recent years, artificial intelligence technology, especially neural networkbased machine learning (ML) technology, has been widely used in MI because of its powerful data processing capability. It provides a feasible strategy to deal with large and complex data for the requirement of MI. In this review, we will focus on the applications of ML methods in OMI and nuclear medical imaging.

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Artificial intelligence‐based versus manual assessment of prostate cancer in the prostate gland: a method comparison study

Cited by 31 publications

References 28 publications

RECOMIA—a cloud-based platform for artificial intelligence research in nuclear medicine and radiology

RECOMIA—a cloud-based platform for artificial intelligence research in nuclear medicine and radiology

Artificial intelligence‐based detection of lymph node metastases by PET/CT predicts prostate cancer‐specific survival

A review of the application of machine learning in molecular imaging

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