Can computer-aided diagnosis assist in the identification of prostate cancer on prostate MRI? a multi-center, multi-reader investigation.

For prostate cancer detection on prostate multiparametric MRI (mpMRI), the Prostate Imaging-Reporting and Data System version 2 (PI-RADSv2) and computer-aided diagnosis (CAD) systems aim to widely improve standardization across radiologists and centers. Our goal was to evaluate CAD assistance in prostate cancer detection compared with conventional mpMRI interpretation in a diverse dataset acquired from five institutions tested by nine readers of varying experience levels, in total representing 14 globally spread institutions. Index lesion sensitivities of mpMRI-alone were 79% (whole prostate (WP)), 84% (peripheral zone (PZ)), 71% (transition zone (TZ)), similar to CAD at 76% (WP, p=0.39), 77% (PZ, p=0.07), 79% (TZ, p=0.15). Greatest CAD benefit was in TZ for moderately-experienced readers at PI-RADSv2 <3 (84% vs mpMRI-alone 67%, p=0.055). Detection agreement was unchanged but CAD-assisted read times improved (4.6 vs 3.4 minutes, p<0.001). At PI-RADSv2 ≥ 3, CAD improved patient-level specificity (72%) compared to mpMRI-alone (45%, p<0.001). PI-RADSv2 and CAD-assisted mpMRI interpretations have similar sensitivities across multiple sites and readers while CAD has potential to improve specificity and moderately-experienced radiologists' detection of more difficult tumors in the center of the gland. The multi-institutional evidence provided is essential to future prostate MRI and CAD development

Optimum imaging strategies for advanced prostate cancer: ASCO guideline

PURPOSE Provide evidence- and expert-based recommendations for optimal use of imaging in advanced prostate cancer. Due to increases in research and utilization of novel imaging for advanced prostate cancer, this guideline is intended to outline techniques available and provide recommendations on appropriate use of imaging for specified patient subgroups. METHODS An Expert Panel was convened with members from ASCO and the Society of Abdominal Radiology, American College of Radiology, Society of Nuclear Medicine and Molecular Imaging, American Urological Association, American Society for Radiation Oncology, and Society of Urologic Oncology to conduct a systematic review of the literature and develop an evidence-based guideline on the optimal use of imaging for advanced prostate cancer. Representative index cases of various prostate cancer disease states are presented, including suspected high-risk disease, newly diagnosed treatment-naïve metastatic disease, suspected recurrent disease after local treatment, and progressive disease while undergoing systemic treatment. A systematic review of the literature from 2013 to August 2018 identified fully published English-language systematic reviews with or without meta-analyses, reports of rigorously conducted phase III randomized controlled trials that compared $ 2 imaging modalities, and noncomparative studies that reported on the efficacy of a single imaging modality. RESULTS A total of 35 studies met inclusion criteria and form the evidence base, including 17 systematic reviews with or without meta-analysis and 18 primary research articles. RECOMMENDATIONS One or more of these imaging modalities should be used for patients with advanced prostate cancer: conventional imaging (defined as computed tomography [CT], bone scan, and/or prostate magnetic resonance imaging [MRI]) and/or next-generation imaging (NGI), positron emission tomography [PET], PET/CT, PET/MRI, or whole-body MRI) according to the clinical scenario

Computer extracted gland features from H&E predicts prostate cancer recurrence comparably to a genomic companion diagnostic test: a large multi-site study

Existing tools for post-radical prostatectomy (RP) prostate cancer biochemical recurrence (BCR) prognosis rely on human pathologist-derived parameters such as tumor grade, with the resulting inter-reviewer variability. Genomic companion diagnostic tests such as Decipher tend to be tissue destructive, expensive, and not routinely available in most centers. We present a tissue non-destructive method for automated BCR prognosis, termed "Histotyping", that employs computational image analysis of morphologic patterns of prostate tissue from a single, routinely acquired hematoxylin and eosin slide. Patients from two institutions (n = 214) were used to train Histotyping for identifying high-risk patients based on six features of glandular morphology extracted from RP specimens. Histotyping was validated for post-RP BCR prognosis on a separate set of n = 675 patients from five institutions and compared against Decipher on n = 167 patients. Histotyping was prognostic of BCR in the validation set (p < 0.001, univariable hazard ratio [HR] = 2.83, 95% confidence interval [CI]: 2.03-3.93, concordance index [c-index] = 0.68, median years-to-BCR: 1.7). Histotyping was also prognostic in clinically stratified subsets, such as patients with Gleason grade group 3 (HR = 4.09) and negative surgical margins (HR = 3.26). Histotyping was prognostic independent of grade group, margin status, pathological stage, and preoperative prostate-specific antigen (PSA) (multivariable p < 0.001, HR = 2.09, 95% CI: 1.40-3.10, n = 648). The combination of Histotyping, grade group, and preoperative PSA outperformed Decipher (c-index = 0.75 vs. 0.70, n = 167). These results suggest that a prognostic classifier for prostate cancer based on digital images could serve as an alternative or complement to molecular-based companion diagnostic tests

Is Artificial Intelligence Replacing Our Radiology Stars in Prostate Magnetic Resonance Imaging? The Stars Do Not Look Big, But They Can Look Brighter

In this issue of European Urology Open Science, Cacciamani and colleagues [1] report preliminary results from their systematic review and diagnostic meta-analysis addressing the lack of data on detection of prostate cancer via multiparametric magnetic resonance imaging (MRI) with and without the assistance of artificial intelligence (AI). They included in their analysis five studies comparing the performance of radiologists and AI alone versus a combination of radiologists aided by a computer-aided diagnosis (CAD) AI system. Interestingly, their analysis shows that the pooled sensitivity (89.1% vs 79.5%) and specificity (78.1% vs 73.1%) were higher for the radiologists + CAD AI combination than for radiologists alone. The pooled diagnostic odds ratio for radiologists + CAD AI was also higher than for radiologists alone (29% vs 11%)

A unique case of ectopic Cushing’s syndrome from a thymic neuroendocrine carcinoma

Ectopic adrenocorticotropic hormone (ACTH) production leading to ectopic ACTH syndrome accounts for a small proportion of all Cushing’s syndrome (CS) cases. Thymic neuroendocrine tumors are rare neoplasms that may secrete ACTH leading to rapid development of hypercortisolism causing electrolyte and metabolic abnormalities, uncontrolled hypertension and an increased risk for opportunistic infections. We present a unique case of a patient who presented with a mediastinal mass, revealed to be an ACTH-secreting thymic neuroendocrine tumor (NET) causing ectopic CS. As the diagnosis of CS from ectopic ACTH syndrome (EAS) remains challenging, we emphasize the necessity for high clinical suspicion in the appropriate setting, concordance between biochemical, imaging and pathology findings, along with continued vigilant monitoring for recurrence after definitive treatment

Staging and Restaging of Rectal Cancer with MRI: A Pictorial Review

© 2022 Elsevier Inc.MRI plays an integral role in the initial local staging of rectal cancer and assessment of treatment response, with the goal of treatment to minimize local recurrence. Standard treatment of rectal cancer includes surgical excision with the addition of neoadjuvant chemoradiation therapy for locally advanced disease. MRI is ideally suited for both surgical planning and risk stratification, allowing for accurate evaluation of tumor location and characteristics, T and N staging, and other MRI-specific features. The role of MRI in risk stratification continues to expand with the emergence of novel organ-sparing management options including active surveillance, minimally invasive surgery, and alternative neoadjuvant therapies. Thus, optimal MRI interpretation requires precise evaluation of the primary tumor and its relationship to surrounding structures with a familiarity of the concepts important in risk stratification and treatment management. Additionally, recognition of the imaging modality's current challenges and limitations can prevent interpretive errors and optimize its diagnostic utility. This pictorial review discusses key concepts of MRI in the initial staging of rectal cancer, assessment of treatment response, and active surveillance of disease, including a focus and discussion on current interpretive challenges and opportunities for advancement