Development and Validation of a Deep Learning Model Based on MRI and Clinical Characteristics to Predict Risk of Prostate Cancer Progression.

Purpose To validate a deep learning (DL) model for predicting the risk of prostate cancer (PCa) progression based on MRI and clinical parameters and compare it with established models. Materials and Methods This retrospective study included 1607 MRI scans of 1143 male patients (median age, 64 years; IQR, 59-68 years) undergoing MRI for suspicion of clinically significant PCa (csPCa) (International Society of Urological Pathology grade > 1) between January 2012 and May 2022 who were negative for csPCa at baseline MRI. A DL model was developed using baseline MRI and clinical parameters (age, prostate-specific antigen [PSA] level, PSA density, and prostate volume) to predict the time to PCa progression (defined as csPCa diagnosis at follow-up).

Retrospective Radiology Research: Do We Need Informed Patient Consent?

While knowledge of the population's view on the need for informed consent for retrospective radiology research may provide valuable insight into how an optimal balance can be achieved between patient rights versus an expedited advancement of radiology science, this is a topic that has been ignored in the literature so far. To investigate the view of the general population, survey data were collected from 2407 people representative of the Dutch population. The results indicate that for non-commercial institutions, especially hospitals (97.

Patient perspectives on the use of artificial intelligence in prostate cancer diagnosis on MRI.

Objectives: This study investigated patients' acceptance of artificial intelligence (AI) for diagnosing prostate cancer (PCa) on MRI scans and the factors influencing their trust in AI diagnoses.

Materials And Methods: A prospective, multicenter study was conducted between January and November 2023. Patients undergoing prostate MRI were surveyed about their opinions on hypothetical AI assessment of their MRI scans.

Prostate MRI and artificial intelligence during active surveillance: should we jump on the bandwagon?

Objective: To review the components of past and present active surveillance (AS) protocols, provide an overview of the current studies employing artificial intelligence (AI) in AS of prostate cancer, discuss the current challenges of AI in AS, and offer recommendations for future research.

Methods: Research studies on the topic of MRI-based AI were reviewed to summarize current possibilities and diagnostic accuracies for AI methods in the context of AS. Established guidelines were used to identify possibilities for future refinement using AI.

Artificial intelligence and radiologists in prostate cancer detection on MRI (PI-CAI): an international, paired, non-inferiority, confirmatory study.

Background: Artificial intelligence (AI) systems can potentially aid the diagnostic pathway of prostate cancer by alleviating the increasing workload, preventing overdiagnosis, and reducing the dependence on experienced radiologists. We aimed to investigate the performance of AI systems at detecting clinically significant prostate cancer on MRI in comparison with radiologists using the Prostate Imaging-Reporting and Data System version 2.1 (PI-RADS 2.

What makes a good scientific presentation on artificial intelligence in medical imaging?

Purpose: Adequate communication of scientific findings is crucial to enhance knowledge transfer. This study aimed to determine the key features of a good scientific oral presentation on artificial intelligence (AI) in medical imaging.

Methods: A total of 26 oral presentations dealing with original research on AI studies in medical imaging at the 2023 RSNA annual meeting were included and systematically assessed by three observers.

Assessing deep learning reconstruction for faster prostate MRI: visual vs. diagnostic performance metrics.

Objective: Deep learning (DL) MRI reconstruction enables fast scan acquisition with good visual quality, but the diagnostic impact is often not assessed because of large reader study requirements. This study used existing diagnostic DL to assess the diagnostic quality of reconstructed images.

Materials And Methods: A retrospective multisite study of 1535 patients assessed biparametric prostate MRI between 2016 and 2020.

Using deep learning to optimize the prostate MRI protocol by assessing the diagnostic efficacy of MRI sequences.

Purpose: To explore diagnostic deep learning for optimizing the prostate MRI protocol by assessing the diagnostic efficacy of MRI sequences.

Method: This retrospective study included 840 patients with a biparametric prostate MRI scan. The MRI protocol included a T2-weighted image, three DWI sequences (b50, b400, and b800 s/mm), a calculated ADC map, and a calculated b1400 sequence.

What is the added value of specialist radiology review of multidisciplinary team meeting cases in a tertiary care center?

Purpose: Multidisciplinary team meetings (MDTMs) are an important component of the workload of radiologists. This study investigated how often subspecialized radiologists change patient management in MDTMs at a tertiary care institution.

Materials And Methods: Over 2 years, six subspecialty radiologists documented their contributions to MDTMs at a tertiary care center.

Does FDG-PET/CT for incidentally found pulmonary lesions lead to a cascade of more incidental findings?

Objective: To determine the frequency, nature, and downstream healthcare costs of new incidental findings that are found on whole-body FDG-PET/CT in patients with a non-FDG-avid pulmonary lesion ≥10 mm that was incidentally found on previous imaging.

Materials And Methods: This retrospective study included a consecutive series of patients who underwent whole-body FDG-PET/CT because of an incidentally found pulmonary lesion ≥10 mm.

Results: Seventy patients were included, of whom 23 (32.

Machine learning-based radiomic analysis and growth visualization for ablation site recurrence diagnosis in follow-up CT.

Objectives: Detecting ablation site recurrence (ASR) after thermal ablation remains a challenge for radiologists due to the similarity between tumor recurrence and post-ablative changes. Radiomic analysis and machine learning methods may show additional value in addressing this challenge. The present study primarily sought to determine the efficacy of radiomic analysis in detecting ASR on follow-up computed tomography (CT) scans.

A framework to integrate artificial intelligence training into radiology residency programs: preparing the future radiologist.

Objectives: To present a framework to develop and implement a fast-track artificial intelligence (AI) curriculum into an existing radiology residency program, with the potential to prepare a new generation of AI conscious radiologists.

Methods: The AI-curriculum framework comprises five sequential steps: (1) forming a team of AI experts, (2) assessing the residents' knowledge level and needs, (3) defining learning objectives, (4) matching these objectives with effective teaching strategies, and finally (5) implementing and evaluating the pilot. Following these steps, a multidisciplinary team of AI engineers, radiologists, and radiology residents designed a 3-day program, including didactic lectures, hands-on laboratory sessions, and group discussions with experts to enhance AI understanding.

Is work overload associated with diagnostic errors on F-FDG-PET/CT?

Purpose: To determine the association between workload and diagnostic errors on F-FDG-PET/CT.

Materials And Methods: This study included 103 F-FDG-PET/CT scans with a diagnostic error that was corrected with an addendum between March 2018 and July 2023. All scans were performed at a tertiary care center.

Can we revolutionize diagnostic imaging by keeping Pandora's box closed?

Incidental imaging findings are a considerable health problem, because they generally result in low-value and potentially harmful care. Healthcare professionals struggle how to deal with them, because once detected they can usually not be ignored. In this opinion article, we first reflect on current practice, and then propose and discuss a new potential strategy to pre-emptively tackle incidental findings.

Complexities of deep learning-based undersampled MR image reconstruction.

Artificial intelligence has opened a new path of innovation in magnetic resonance (MR) image reconstruction of undersampled k-space acquisitions. This review offers readers an analysis of the current deep learning-based MR image reconstruction methods. The literature in this field shows exponential growth, both in volume and complexity, as the capabilities of machine learning in solving inverse problems such as image reconstruction are explored.

European Association of Nuclear Medicine Focus 5: Consensus on Molecular Imaging and Theranostics in Prostate Cancer.

Background: In prostate cancer (PCa), questions remain on indications for prostate-specific membrane antigen (PSMA) positron emission tomography (PET) imaging and PSMA radioligand therapy, integration of advanced imaging in nomogram-based decision-making, dosimetry, and development of new theranostic applications.

Objective: We aimed to critically review developments in molecular hybrid imaging and systemic radioligand therapy, to reach a multidisciplinary consensus on the current state of the art in PCa.

Design, Setting, And Participants: The results of a systematic literature search informed a two-round Delphi process with a panel of 28 PCa experts in medical or radiation oncology, urology, radiology, medical physics, and nuclear medicine.

Work overload and diagnostic errors in radiology.

Purpose: To determine the association between workload and diagnostic errors on clinical CT scans.

Method: This retrospective study was performed at a tertiary care center and covered the period from January 2020 to March 2023. All clinical CT scans that contained an addendum describing a perceptual error (i.

The Effect of Image Resampling on the Performance of Radiomics-Based Artificial Intelligence in Multicenter Prostate MRI.

Background: Single center MRI radiomics models are sensitive to data heterogeneity, limiting the diagnostic capabilities of current prostate cancer (PCa) radiomics models.

Purpose: To study the impact of image resampling on the diagnostic performance of radiomics in a multicenter prostate MRI setting.

Study Type: Retrospective.

Focused view CT angiography for selective visualization of stroke related arteries: technical feasibility.

Objectives: This study investigated the technical feasibility of focused view CTA for the selective visualization of stroke related arteries.

Methods: A total of 141 CTA examinations for acute ischemic stroke evaluation were divided into a set of 100 cases to train a deep learning algorithm (dubbed "focused view CTA") that selectively extracts brain (including intracranial arteries) and extracranial arteries, and a test set of 41 cases. The visibility of anatomic structures at focused view and unmodified CTA was assessed using the following scoring system: 5 = completely visible, diagnostically sufficient; 4 = nearly completely visible, diagnostically sufficient; 3 = incompletely visible, barely diagnostically sufficient; 2 = hardly visible, diagnostically insufficient; 1 = not visible, diagnostically insufficient.

Why don't we inform patients about the risk of diagnostic errors?

The principles of autonomy and informed consent dictate that patients who undergo a radiological examination should actually be informed about the risk of diagnostic errors. Implementing such a policy could potentially increase the quality of care. However, due to the vast number of radiological examinations that are performed in each hospital each day, financial constraints, and the risk of losing trust, patients, and income if the requirement for informed consent is not imposed by law on a state or national level, it may be challenging to inform patients about the risk of diagnostic errors.

A new medical imaging postprocessing and interpretation concept to investigate the clinical relevance of incidentalomas: can we keep Pandora's box closed?

Background: Incidental imaging findings (incidentalomas) are common, but there is currently no effective means to investigate their clinical relevance.

Purpose: To introduce a new concept to postprocess a medical imaging examination in a way that incidentalomas are concealed while its diagnostic potential is maintained to answer the referring physician's clinical questions.

Material And Methods: A deep learning algorithm was developed to automatically eliminate liver, gallbladder, pancreas, spleen, adrenal glands, lungs, and bone from unenhanced computed tomography (CT).