Single Center Experience with a 4-Week 177Lu-PSMA-617 Treatment Interval in Patients with Metastatic Castration-Resistant Prostate Cancer

Background: 177Lu-PSMA-617 is a promising theragnostic treatment for metastatic castration-resistant prostate cancer (mCRPC). However, both the optimal treatment dose and interval in mCRPC and the rate of identification of responders from non-responders among possible treatment candidates are unknown. Methods: 62 men with mCRPC who were treated with 177Lu-PSMA-617 during 1/2017–2/2019 were included in the study. Treatment responses, overall survival (OS) and progression free survival (PFS) were determined. The median follow-up time was 1.4 years (IQR 0.5–2.2). Tumor volume of metastases (MTV), SUVmax and tumor lesion activity (TLA) were quantitated from pre-treatment PSMA PET/CT images together with pre-treatment PSA. Results: An average of three treatment cycles (2–5) were given within a four-week interval. PFS was 4.9 months (2.4–9.6) and OS was 17.2 months (6–26.4). There were no major adverse events reported. A significant PSA response of >50% was found in 58.7% of patients, which was significantly associated with longer OS, p < 0.004. PSA response was not associated with staging PSMA-derived parameters. Conclusions: 177Lu-PSMA-617 treatment in four-week intervals was safe and effective. Almost 60% of patients had a significant PSA response, which was associated with better OS. Pre-treatment PSA kinetics or staging PSMA PET/CT-derived parameters were not helpful in identifying treatment responders from non-responders; better biomarkers are needed to aid in patient selection.© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

Ga-68-PSMA ligand PET/CT in patients with prostate cancer: How we review and report

Recently, positron emission tomography (PET) imaging using PSMA-ligands has gained high attention as a promising new radiotracer in patients with prostate cancer (PC). Several studies promise accurate staging of primary prostate cancer and restaging after biochemical recurrence with Ga-68-PSMA ligand Positron emission tomography/computed tomography (PET/CT). However, prospective trials and clinical guidelines for this new technique are still missing. Therefore, we summarized our experience with Ga-68-PSMA ligand PET/CT examinations in patients with primary PC and biochemical recurrence. It focuses on the technical and logistical aspects of Ga-68-PSMA ligand PET/CT examination as well as on the specific background for image reading discussing also potential pitfalls. Further, it includes relevant issues on free-text as well as structured reporting used in daily clinical routine

Artificial intelligence in digital pathology: a diagnostic test accuracy systematic review and meta-analysis

Ensuring diagnostic performance of AI models before clinical use is key to the safe and successful adoption of these technologies. Studies reporting AI applied to digital pathology images for diagnostic purposes have rapidly increased in number in recent years. The aim of this work is to provide an overview of the diagnostic accuracy of AI in digital pathology images from all areas of pathology. This systematic review and meta-analysis included diagnostic accuracy studies using any type of artificial intelligence applied to whole slide images (WSIs) in any disease type. The reference standard was diagnosis through histopathological assessment and / or immunohistochemistry. Searches were conducted in PubMed, EMBASE and CENTRAL in June 2022. We identified 2976 studies, of which 100 were included in the review and 48 in the full meta-analysis. Risk of bias and concerns of applicability were assessed using the QUADAS-2 tool. Data extraction was conducted by two investigators and meta-analysis was performed using a bivariate random effects model. 100 studies were identified for inclusion, equating to over 152,000 whole slide images (WSIs) and representing many disease types. Of these, 48 studies were included in the meta-analysis. These studies reported a mean sensitivity of 96.3% (CI 94.1-97.7) and mean specificity of 93.3% (CI 90.5-95.4) for AI. There was substantial heterogeneity in study design and all 100 studies identified for inclusion had at least one area at high or unclear risk of bias. This review provides a broad overview of AI performance across applications in whole slide imaging. However, there is huge variability in study design and available performance data, with details around the conduct of the study and make up of the datasets frequently missing. Overall, AI offers good accuracy when applied to WSIs but requires more rigorous evaluation of its performance.Comment: 26 pages, 5 figures, 8 tables + Supplementary material

Interobserver Agreement of PD-L1/SP142 Immunohistochemistry and Tumor-Infiltrating Lymphocytes (TILs) in Distant Metastases of Triple-Negative Breast Cancer: A Proof-of-Concept Study. A Report on Behalf of the International Immuno-Oncology Biomarker Working Group

Patients with advanced triple-negative breast cancer (TNBC) benefit from treatment with atezolizumab, provided that the tumor contains 651% of PD-L1/SP142-positive immune cells. Numbers of tumor-infiltrating lymphocytes (TILs) vary strongly according to the anatomic localization of TNBC metastases. We investigated inter-pathologist agreement in the assessment of PD-L1/SP142 immunohistochemistry and TILs. Ten pathologists evaluated PD-L1/SP142 expression in a proficiency test comprising 28 primary TNBCs, as well as PD-L1/SP142 expression and levels of TILs in 49 distant TNBC metastases with various localizations. Interobserver agreement for PD-L1 status (positive versus negative) was high in the proficiency test: the corresponding scores as percentages showed good agreement with the consensus diagnosis. In TNBC metastases, there was substantial variability in PD-L1 status at the individual patient level. For one in five patients, the chance of treatment was essentially random, with half of the pathologists designating them as positive and half negative. Assessment of PD-L1/SP142 and TILs as percentages in TNBC metastases showed poor and moderate agreement, respectively. Additional training for metastatic TNBC is required to enhance interobserver agreement. Such training, focusing on metastatic specimens, seems worthwhile, since the same pathologists obtained high percentages of concordance (ranging from 93% to 100%) on the PD-L1 status of primary TNBCs

A Survey on Deep Learning in Medical Image Analysis

Deep learning algorithms, in particular convolutional networks, have rapidly become a methodology of choice for analyzing medical images. This paper reviews the major deep learning concepts pertinent to medical image analysis and summarizes over 300 contributions to the field, most of which appeared in the last year. We survey the use of deep learning for image classification, object detection, segmentation, registration, and other tasks and provide concise overviews of studies per application area. Open challenges and directions for future research are discussed.Comment: Revised survey includes expanded discussion section and reworked introductory section on common deep architectures. Added missed papers from before Feb 1st 201