PSMA PET/CT Accuracy in Diagnosing Prostate Cancer Nodes Metastases.

BACKGROUND/AIM: This study aimed to evaluate the diagnostic accuracy of prostate-specific membrane antigen (PSMA)-directed positron emission tomography/computed tomography (PET/CT) in pelvic nodal staging, using postoperative histopathology data as the reference standard. PATIENTS AND METHODS: From January 2020 to June 2024, 78 patients with clinically significant prostate cancer (PCa) (ISUP Grade Group 2) underwent radical prostatectomy plus extended pelvic lymph node dissection (ePLND): 60 (77%) vs. 18 (23%) men had an intermediate vs. high risk PCa. All the patients underwent PSMA PET/TC before surgery for clinical staging and nodes focal uptake (standardized uptake value "SUVmax) was evaluated to rule out the presence of metastases. RESULTS: PSMA PET/CT was suspicious for nodes metastases in 16/78 (20.5%) men (median SUVmax 26.2), conversely, histology demonstrated nodes metastases in 18/78 (23.1%). PSMA PET/CT was negative for nodal involvement in all Grade Group 2 (GG2) PCa, positive in 4/4 (100%) GG3 PCa, and in 10/14 (71.4%) GG5 PCa. In detail, PSMA PET/CT was false negative in 2/4 PCa, characterized by GG5 plus ductal adenocarcinoma. Overall, PSMA PET/CT sensitivity, specificity, positive and negative predictive value, and diagnostic accuracy in diagnosing nodal metastases were equal to 87.5, 96.8, 87.5 96.7, and 92.3%, respectively. CONCLUSION: PSMA PET/CT demonstrated an overall diagnostic accuracy of 92.3% in nodal staging (100% in GG2 PCa), which decreased to 63.6% in GG5 PCa. In high-risk patients or in case of ductal adenocarcinoma, a negative PSMA PET/CT does not rule out the need for ePLND.

Multimodal representations of biomedical knowledge from limited training whole slide images and reports using deep learning.

The increasing availability of biomedical data creates valuable resources for developing new deep learning algorithms to support experts, especially in domains where collecting large volumes of annotated data is not trivial. Biomedical data include several modalities containing complementary information, such as medical images and reports: images are often large and encode low-level information, while reports include a summarized high-level description of the findings identified within data and often only concerning a small part of the image. However, only a few methods allow to effectively link the visual content of images with the textual content of reports, preventing medical specialists from properly benefitting from the recent opportunities offered by deep learning models. This paper introduces a multimodal architecture creating a robust biomedical data representation encoding fine-grained text representations within image embeddings. The architecture aims to tackle data scarcity (combining supervised and self-supervised learning) and to create multimodal biomedical ontologies. The architecture is trained on over 6,000 colon whole slide Images (WSI), paired with the corresponding report, collected from two digital pathology workflows. The evaluation of the multimodal architecture involves three tasks: WSI classification (on data from pathology workflow and from public repositories), multimodal data retrieval, and linking between textual and visual concepts. Noticeably, the latter two tasks are available by architectural design without further training, showing that the multimodal architecture that can be adopted as a backbone to solve peculiar tasks. The multimodal data representation outperforms the unimodal one on the classification of colon WSIs and allows to halve the data needed to reach accurate performance, reducing the computational power required and thus the carbon footprint. The combination of images and reports exploiting self-supervised algorithms allows to mine databases without needing new annotations provided by experts, extracting new information. In particular, the multimodal visual ontology, linking semantic concepts to images, may pave the way to advancements in medicine and biomedical analysis domains, not limited to histopathology.

Integrated diagnostics, complex biomarkers, and a new frontier for tissue pathology.

What will be the next disruptive technology that will change pathology's routine practice again? In this editorial we make a case for the need of more complex biomarkers in oncology diagnostics, to match the inherent complexity of cancer biology. This complexity will be achieved by the validation of technology able to generate more meaningful biological datapoints (epitomized in tissue pathology by technologies such as multiplex immunofluorescence) and, more important, by the systematic analysis of multimodal technology outputs with artificial intelligence tools, which is the essence of integrated diagnostics. While describing these processes, the authors highlight the pivotal role that histopathology will play, once again, in yet another transformation in diagnostics.

Computer-assisted urine cytology: Faster, cheaper, better?

Recent advancements in computer-assisted diagnosis (CAD) have catalysed significant progress in pathology, particularly in the realm of urine cytopathology. This review synthesizes the latest developments and challenges in CAD for diagnosing urothelial carcinomas, addressing the limitations of traditional urinary cytology. Through a literature review, we identify and analyse CAD models and algorithms developed for urine cytopathology, highlighting their methodologies and performance metrics. We discuss the potential of CAD to improve diagnostic accuracy, efficiency and patient outcomes, emphasizing its role in streamlining workflow and reducing errors. Furthermore, CAD tools have shown potential in exploring pathological conditions, uncovering novel biomarkers and prognostic/predictive features previously unknown or unseen. Finally, we examine the practical issues surrounding the integration of CAD into clinical practice, including regulatory approval, validation and training for pathologists. Despite the promising results, challenges remain, necessitating further research and validation efforts. Overall, CAD presents a transformative opportunity to revolutionize diagnostic practices in urine cytopathology, paving the way for enhanced patient care and outcomes.

Feasibility of MALDI-MSI-Based Proteomics Using Bouin-Fixed Pathology Samples: Untapping the Goldmine of Nephropathology Archives.

The application of innovative spatial proteomics techniques, such as those based upon matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) technology, has the potential to impact research in the field of nephropathology. Notwithstanding, the possibility to apply this technology in more routine diagnostic contexts remains limited by the alternative fixatives employed by this ultraspecialized diagnostic field, where most nephropathology laboratories worldwide use bouin-fixed paraffin-embedded (BFPE) samples. Here, the feasibility of performing MALDI-MSI on BFPE renal tissue is explored, evaluating variability within the trypsin-digested proteome as a result of different preanalytical conditions and comparing them with the more standardized formalin-fixed paraffin-embedded (FFPE) counterparts. A large proportion of the features (270, 68.9%) was detected in both BFPE and FFPE renal samples, demonstrating only limited variability in signal intensity (10.22-10.06%). Samples processed with either fixative were able to discriminate the principal parenchyma regions along with diverse renal substructures, such as glomeruli, tubules, and vessels. This was observed when performing an additional "stress test", showing comparable results in both BFPE and FFPE samples when the distribution of several amyloid fingerprint proteins was mapped. These results suggest the utility of BFPE tissue specimens in MSI-based nephropathology research, further widening their application in this field.

Oncological Outcomes in Men With Favorable Intermediate Risk Prostate Cancer Enrolled in Active Surveillance.

BACKGROUND/AIM: To evaluate the long-term oncological outcomes in men with intermediate risk prostate cancer (PCa) enrolled in active surveillance (AS). PATIENTS AND METHODS: From April 2015 to December 2022, 30 men with Gleason score 3+4/ISUP Grade Group2 (GG2), greatest percentage of cancer (GPC) ≤50%, Gleason pattern 4 ≤10%, ≤3 positive biopsy cores were enrolled in AS. All patients underwent confirmatory transperineal saturation biopsy (SPBx: 20 cores) 12 months from diagnosis plus multiparametric magnetic resonance (mpMRI) evaluation. At the last follow-up, 68Ga prostate-specific membrane antigen (PSMA) positron-emission tomography (PET)/computed tomography (CT) was added: lesions with PIRADS score ≥3 and/or standardized uptake value (SUVmax) >5 were submitted to four targeted cores. RESULTS: Three out of 30 (10%) men with GG2 PCa were reclassified at confirmatory biopsy. At the last follow-up (median 5.2 years), only 2 of 27 (7.4%) men were reclassified and 23/30 (76.6%) continued AS. CONCLUSION: Men with favorable GG2 PCa enrolled in AS have good long-term oncological results. The use of selective criteria (i.e., SPBx, mpMRI, PSMA PET/CT) reduces the risk of reclassification.

The biomarkers ATLAS: An audit on 1100 non-small cell lung cancer from an Italian knowledge-based database.

AIMS: To date, precision medicine has revolutionized the clinical management of Non-Small Cell Lung Cancer (NSCLC). International societies approved a rapidly improved mandatory testing biomarkers panel for the clinical stratification of NSCLC patients, but harmonized procedures are required to optimize the diagnostic workflow. In this context a knowledge-based database (Biomarkers ATLAS, https://biomarkersatlas.com/) was developed by a supervising group of expert pathologists and thoracic oncologists collecting updated clinical and molecular records from about 80 referral Italian institutions. Here, we audit molecular and clinical data from n = 1100 NSCLC patients collected from January 2019 to December 2020. METHODS: Clinical and molecular records from NSCLC patients were retrospectively collected from the two coordinating institutions (University of Turin and University of Naples). Molecular biomarkers (KRAS, EGFR, BRAF, ROS1, ALK, RET, NTRK, MET) and clinical data (sex, age, histological type, smoker status, PD-L1 expression, therapy) were collected and harmonized. RESULTS: Clinical and molecular data from 1100 (n = 552 mutated and n = 548 wild-type) NSCLC patients were systematized and annotated in the ATLAS knowledge-database. Molecular records from biomarkers testing were matched with main patients' clinical variables. CONCLUSIONS: Biomarkers ATLAS (https://biomarkersatlas.com/) represents a unique, easily managing, and reliable diagnostic tool aiming to integrate clinical records with molecular alterations of NSCLC patients in the real-word Italian scenario.

Advances in radiology and pathology of prostate cancer: a review for the pathologist.

Multiparametric magnetic resonance imaging (mpMRI) has improved systematic prostate biopsy procedures in the diagnosis of clinically significant prostate cancer (csPCa) by reducing the number of unnecessary biopsies; numerous level one evidence studies have confirmed the accuracy of MRI-targeted biopsy, but, still today, systematic prostate biopsy is recommended to reduce the 15-20% false negative rate of mpMRI. New advanced imaging has been proposed to detect suspicious lesions and perform targeted biopsies especially when mpMRI cannot be performed. Transrectal ultrasound (TRUS) modalities are emerging as methods with greater sensitivity and specificity for the detection of PCa compared to the traditional TRUS; these techniques include elastography and contrast-enhanced ultrasound, as well as improved B-mode and Doppler techniques. These modalities can be combined to define a novel ultrasound approach: multiparametric ultrasound (mpUS). More recently, micro-ultrasound (MicroUS) and prostate-specific membrane antigen (PSMA) positron emission tomography/computed tomography (PET/CT) have demonstrated to be sensitive for the detection of primary prostatic lesions resulting highly correlated with the aggressiveness of the primary prostatic tumor. In parallel, artificial intelligence is advancing and is set out to deeply change both radiology and pathology. In this study we address the role, advantages and shortcomings of novel imaging techniques for Pca, and discuss future directions including the applications of artificial intelligence-based techniques to imaging as well as histology. The significance of these findings for the practicing pathologist is discussed.

Ductal prostate cancer staging: Role of PSMA PET/CT.

INTRODUCTION: To evaluate the accuracy of PSMA PET/CT in the diagnosis and clinical staging of prostatic ductal adenocarcinoma (DAC). MATERIALS AND METHODS: Two Caucasian men 58 and 62 years old were admitted to our Department for dysuria: the patients had not familiarity for prostate cancer (PCa), PSA values were 5.6 and 2.8 ng/ml, digital rectal examination was positive, multiparametric magnetic resonance image (mpMRI) showed for both the presence of an index lesion PIRADS score 5. The patients underwent extended transperineal prostate biopsy combined with four mpMRI/TRUS fusion biopsy under sedation and antibiotic prophylaxis; biopsy histology demonstrated the presence of a mixed PCa characterized by DAC and acinar PCa (Grade Group 4/Gleason score 8). The patients underwent clinical staging performing lung and abdominal CT, bone scan and fluoride 18 (18F) PSMA PET/CT. RESULTS: Conventional imaging was negative for distant metastases; 18F-PSMA PET/CT showed in both patients an intraprostatic lesion characterized by a standardized uptake value (SUVmax) equal to 4.6 and 4.9 in the absence of distant lesions suspicious for metastases. Following multidisciplinary evaluation, the patients underwent radical prostatectomy plus extended pelvic lymphadenectomy. Definitive specimen showed the presence in both cases of a mixed pT3bN1 PCa (ductal plus acinar pattern Grade Group 4) with positive surgical margins, neuronal invasion, and nodes metastases (5/20 and 6/24, respectively). Post-operative PSA in the two patients was 0.8 and 0.3 ng/ml, therefore patients underwent adjuvant therapy. CONCLUSIONS: Conventional imaging and PSMA PET/CT could result inadequate in clinical staging of DAC, the use of more imaging data (i.e. mpMRI and/or F-18 FDG) could improve overall accuracy.

Improvements in digital pathology equipment for renal biopsies: updating the standard model.

INTRODUCTION: Digital pathology can improve the technical and interpretative workflows in nephropathology by creating hub-spoke networks and virtuous collaboration projects among centers in different geographical regions. New high-resolution fast-scanning instruments combined with currently existing equipment were tested in a nephropathology hub to evaluate possible upgrading in the routine processing phases. METHODS: The scanning performance of two different instruments (Aperio vs hybrid MIDI II) was evaluated and a comparative quality control check was performed on obtained whole slide images. RESULTS: Both with default and custom settings for light microscopy, MIDI II proved to be faster, with only slightly more time required to prepare the scan and larger final file size as compared to Aperio (p < 0.001). No differences were noted in the number of out-of-focus slides per case (p = 0.75). Regarding immunofluorescence, the new scanner required longer preparation time (p = 0.001) with comparable scanning times and final file size (p = 0.169 and p = 0.177, respectively). Quality control showed differences in 3 quality features related to white background and blurriness (p < 0.001). No major discordances in the final diagnosis were recorded after comparing the report obtained with slides scanned using the two instruments, with only one case (4%) showing minor disagreement. CONCLUSION: The present report describes the experience of a hub nephropathology center adopting next generation digital pathology tools for the routine assessment of renal biopsies, highlighting the need for a complementary approach towards a philosophy of interoperability.

Standardized and simplified reporting of next-generation sequencing results in advanced non-small-cell lung cancer: Practical indications from an Italian multidisciplinary group.

Molecular biomarker testing is increasingly becoming standard of care for advanced non-small cell lung cancer (NSCLC). Tissue and liquid biopsy-based next-generation sequencing (NGS) is now highly recommended and has become an integral part of the routine management of advanced NSCLC patients. This highly sensitive approach can simultaneously and efficiently detect multiple biomarkers even in scant samples. However full optimization of NGS in clinical practice requires accurate reporting and interpretation of NGS findings. Indeed, as the number of NSCLC biomarkers continues to grow, clinical reporting of NGS data is becoming increasingly complex. In this scenario, achieving standardization, simplification, and improved readability of NGS reports is key to ensuring timely and appropriate treatment decisions. In an effort to address the complexity and lengthy reporting of NGS mutation results, an Italian group of 14 healthcare professionals involved in NSCLC management convened in 2023 to address the content, structure, and ease-of-use of NGS reporting practices and proposed a standard report template for clinical use This article presents the key discussion points addressed by the Italian working group and describes the essential elements of the report template.

Modelling digital health data: The ExaMode ontology for computational pathology.

Computational pathology can significantly benefit from ontologies to standardize the employed nomenclature and help with knowledge extraction processes for high-quality annotated image datasets. The end goal is to reach a shared model for digital pathology to overcome data variability and integration problems. Indeed, data annotation in such a specific domain is still an unsolved challenge and datasets cannot be steadily reused in diverse contexts due to heterogeneity issues of the adopted labels, multilingualism, and different clinical practices. Material and methods: This paper presents the ExaMode ontology, modeling the histopathology process by considering 3 key cancer diseases (colon, cervical, and lung tumors) and celiac disease. The ExaMode ontology has been designed bottom-up in an iterative fashion with continuous feedback and validation from pathologists and clinicians. The ontology is organized into 5 semantic areas that defines an ontological template to model any disease of interest in histopathology. Results: The ExaMode ontology is currently being used as a common semantic layer in: (i) an entity linking tool for the automatic annotation of medical records; (ii) a web-based collaborative annotation tool for histopathology text reports; and (iii) a software platform for building holistic solutions integrating multimodal histopathology data. Discussion: The ontology ExaMode is a key means to store data in a graph database according to the RDF data model. The creation of an RDF dataset can help develop more accurate algorithms for image analysis, especially in the field of digital pathology. This approach allows for seamless data integration and a unified query access point, from which we can extract relevant clinical insights about the considered diseases using SPARQL queries.

Congo Red Staining in Digital Pathology: The Streamlined Pipeline for Amyloid Detection Through Congo Red Fluorescence Digital Analysis.

Renal amyloidosis is a rare condition caused by the progressive accumulation of misfolded proteins within glomeruli, vessels, and interstitium, causing functional decline and requiring prompt treatment due to its significant morbidity and mortality. Congo red (CR) stain on renal biopsy samples is the gold standard for diagnosis, but the need for polarized light is limiting the digitization of this nephropathology field. This study explores the feasibility and reliability of CR fluorescence on virtual slides (CRFvs) in evaluating the diagnostic accuracy and proposing an automated digital pipeline for its assessment. Whole-slide images from 154 renal biopsies with CR were scanned through a Texas red fluorescence filter (NanoZoomer S60, Hamamatsu) at the digital Nephropathology Center of the Istituto di Ricovero e Cura a Carattere Scientifico San Gerardo, Monza, Italy, and evaluated double-blinded for the detection and quantification through the amyloid score and a custom ImageJ pipeline was built to automatically detect amyloid-containing regions. Interobserver agreement for CRFvs was optimal (k = 0.90; 95% CI, 0.81-0.98), with even better concordance when consensus-based CRFvs evaluation was compared to the standard CR birefringence (BR) (k = 0.98; 95% CI, 0.93-1). Excellent performance was achieved in the assessment of amyloid score overall by CRFvs (weighted k = 0.70; 95% CI, 0.08-1), especially within the interstitium (weighted k = 0.60; 95% CI, 0.35-0.84), overcoming the misinterpretation of interstitial and capsular collagen BR. The application of an automated digital pathology pipeline (Streamlined Pipeline for Amyloid detection through CR fluorescence Digital Analysis, SPADA) further increased the performance of pathologists, leading to a complete concordance with the standard BR. This study represents an initial step in the validation of CRFvs, demonstrating its general reliability in a digital nephropathology center. The computational method used in this study has the potential to facilitate the integration of spatial omics and artificial intelligence tools for the diagnosis of amyloidosis, streamlining its detection process.

Digital Examination of LYmph node CYtopathology Using the Sydney system (DELYCYUS): An international, multi-institutional study.

BACKGROUND: After a series of standardized reporting systems in cytopathology, the Sydney system was recently introduced to address the need for reproducibility and standardization in lymph node cytopathology. Since then, the risk of malignancy for the categories of the Sydney system has been explored by several studies, but no studies have yet examined the interobserver reproducibility of the Sydney system. METHODS: The authors assessed interobserver reproducibility of the Sydney system on 85 lymph node fine-needle aspiration cytology cases reviewed by 15 cytopathologists from 12 institutions in eight different countries, resulting in 1275 diagnoses. In total, 186 slides stained with Diff-Quik, Papanicolaou, and immunocytochemistry were scanned. A subset of the cases included clinical data and results from ultrasound examinations, flow cytometry immunophenotyping, and fluorescence in situ hybridization analysis. The study participants assessed the cases digitally using whole-slide images. RESULTS: Overall, the authors observed an almost perfect agreement of cytopathologists with the ground truth (median weighted Cohen κ = 0.887; interquartile range, κ = 0.210) and moderate overall interobserver concordance (Fleiss κ = 0.476). There was substantial agreement for the inadequate and malignant categories (κ = 0.794 and κ = 0.729, respectively), moderate agreement for the benign category (κ = 0.490), and very slight agreement for the suspicious (κ = 0.104) and atypical (κ = 0.075) categories. CONCLUSIONS: The Sydney system for reporting lymph node cytopathology shows adequate interobserver concordance. Digital microscopy is an adequate means to assess lymph node cytopathology specimens.

Validation of full-remote reporting for cervicovaginal cytology: the Caltagirone-Acireale distributed lab.

INTRODUCTION: Cervical cancer is the fourth most common cancer in women, and its prevention is based on vaccination and screening. Screening consists of molecular human papillomavirus (HPV) testing and cytologic analysis of cervical smears, which require expensive equipment and the interaction of numerous professionals such as biologists, cytologists, laboratory technicians, and pathologists. MATERIALS AND METHODS: We centralize the cervical samples from more than 51 clinics in 1 main laboratory, where automated HPV testing is performed. HPV-positive cases are collected and used to prepare a liquid-based cytology slide, which is stained and immediately scanned. The resulting whole-slide images (WSIs) are immediately available in a remote laboratory where they are examined by experienced cytologists using virtual microscopy. This setup was validated by making each of the 3 readers independently diagnose 506 specimens in random order, using both conventional light microscopy (CLM) and WSIs, with a minimum wash-out period of 3 weeks and with a final discussion for all cases. RESULTS: Intraobserver agreement among CLM and WSI ranged from 0.71 to 0.79, and interobserver agreement for the 3 readers compared with the consensus diagnosis was similar for the 2 modes of assessment. Readers subjectively felt confident in their WSI diagnosis for inadequate and negative cases, but less so in other cases. The perceived difficulty was slightly higher in WSI readings. CONCLUSIONS: Interobserver agreement in cervicovaginal cytology is moderate and does not vary if the slides are examined conventionally or digitally. Despite higher reported subjective difficulty and lower confidence in the WSI diagnosis, we did not observe a deterioration in diagnostic performance using WSI compared with CLM.

Deep learning for multi-class semantic segmentation enables colorectal cancer detection and classification in digital pathology images.

In colorectal cancer (CRC), artificial intelligence (AI) can alleviate the laborious task of characterization and reporting on resected biopsies, including polyps, the numbers of which are increasing as a result of CRC population screening programs ongoing in many countries all around the globe. Here, we present an approach to address two major challenges in the automated assessment of CRC histopathology whole-slide images. We present an AI-based method to segment multiple ([Formula: see text]) tissue compartments in the H &E-stained whole-slide image, which provides a different, more perceptible picture of tissue morphology and composition. We test and compare a panel of state-of-the-art loss functions available for segmentation models, and provide indications about their use in histopathology image segmentation, based on the analysis of (a) a multi-centric cohort of CRC cases from five medical centers in the Netherlands and Germany, and (b) two publicly available datasets on segmentation in CRC. We used the best performing AI model as the basis for a computer-aided diagnosis system that classifies colon biopsies into four main categories that are relevant pathologically. We report the performance of this system on an independent cohort of more than 1000 patients. The results show that with a good segmentation network as a base, a tool can be developed which can support pathologists in the risk stratification of colorectal cancer patients, among other possible uses. We have made the segmentation model available for research use on https://grand-challenge.org/algorithms/colon-tissue-segmentation/ .

68Ga-PSMA PET/CT evaluation in men enrolled in prostate cancer Active Surveillance.

INTRODUCTION: To evaluate the accuracy of 68Ga-prostate specific membrane antigen (PSMA) positron emission tomography/computed tomography (PET/CT) in the diagnosis of clinically significant prostate cancer (csPCa: Grade Group ≥ 2) in men enrolled in Active Surveillance (AS) protocol. MATERIALS AND METHODS: From May 2013 to December 2021 200 men aged between 52 and 74 years (median age 63) with very low risk PCa were enrolled in an AS protocol study. During the follow up 48/200 (24%) men were upgraded and 10/200 (5%) decided to leave the AS protocol. After five years from confirmatory biopsy (range: 48-60 months) 40/142 (28.2%) consecutive patients were submitted to mpMRI and 68Ga-PSMA PET/CT imaging examinations before scheduled repeated biopsy. All the mpMRI (PI-RADS ≥ 3) and 68Ga-PET/TC standardized uptake value (SUVmax) ≥ 5 index lesions underwent targeted cores (mpMRI-TPBx and PSMA-TPBx) combined with transperineal saturation prostate biopsy (SPBx: median 20 cores). RESULTS: Multiparametric MRI and 68Ga-PSMA PET/CT showed 18/40 (45%) and 9/40 (22.5%) lesions suspicious for PCa. In 3/40 (7.5%) men a csPCa (GG2) was found; 68Ga-PSMA-TPBx vs. mpMRI-TPBx vs. SPBx diagnosed 2/3 (66.6%) vs. 2/3 (66.6%) vs. 3/3 (100%) csPCa, respectively. In detail, mpMRI and 68Ga-PSMA PET/TC demonstrated 16/40 (40%) vs. 7/40 (17.5%) false positive and 1 (33.3%) vs. 1 (33.3%) false negative results. CONCLUSION: Although 68PSMA PET/CT did not improve the detection for csPCa of SPBx (1 false negative result equal to 33.3% of the cases), at the same time, would have spared 31/40 (77.5%) scheduled biopsies showing a better diagnostic accuracy in comparison with mpMRI (83.3% vs. 70.2%).

68Ga-PSMA PET/CT and Prostate Cancer Diagnosis: Which SUVmax Value?

BACKGROUND/AIM: To evaluate the diagnostic accuracy of 68Ga-prostate-specific membrane antigen (PSMA) positron emission tomography/computed tomography (PET/CT) in the diagnosis and staging of prostate cancer (PCa). PATIENTS AND METHODS: From January 2021 to December 2022, 160 men (median age: 66 years) with PCa (median PSA of 11.7 ng/ml) before prostate biopsy underwent 68Ga-PET/CT imaging examinations (Biograph 6; Siemens, Knoxville, TN, USA). The location of focal uptake on 68Ga-PSMA PET/TC and standardized uptake values (SUVmax) were reported on a per-lesion basis for each International Society of Urological Pathology (ISUP) grade group (GG) PCa. RESULTS: Overall, the median intraprostatic 68Ga-PSMA SUVmax was 26.1 (range=2.7-164); in the 15 men with not clinically significant PCa (ISUP grade group 1) median SUVmax was 7.5 (range=2.7-12.5). In the 145 men with csPCa (ISUP GG≥2) median SUVmax was 33 (range=7.8-164). A SUVmax cut-off of 8 demonstrated a diagnostic accuracy in the diagnosis of PCa equal to 87.7% vs. 89.3% vs. 100% in the presence of a GG1 vs. GG2 vs. GG≥3 PCa, respectively. In addition, median SUVmax in the bone and node metastases was 52.7 (range=25.3-92.8) and 47 (range=24.5-65), respectively. CONCLUSION: 68GaPSMA PET/CT with a SUVmax cut-off of 8 demonstrated a good accuracy in the diagnosis of csPCa (100% in the presence of GG≥3) showing a good cost-benefit ratio as a single procedure for the diagnosis and staging of high-risk PCa.

Impact of Stain Normalization on Pathologist Assessment of Prostate Cancer: A Comparative Study.

In clinical routine, the quality of whole-slide images plays a key role in the pathologist's diagnosis, and suboptimal staining may be a limiting factor. The stain normalization process helps to solve this problem through the standardization of color appearance of a source image with respect to a target image with optimal chromatic features. The analysis is focused on the evaluation of the following parameters assessed by two experts on original and normalized slides: (i) perceived color quality, (ii) diagnosis for the patient, (iii) diagnostic confidence and (iv) time required for diagnosis. Results show a statistically significant increase in color quality in the normalized images for both experts (p < 0.0001). Regarding prostate cancer assessment, the average times for diagnosis are significantly lower for normalized images than original ones (first expert: 69.9 s vs. 77.9 s with p < 0.0001; second expert: 37.4 s vs. 52.7 s with p < 0.0001), and at the same time, a statistically significant increase in diagnostic confidence is proven. The improvement of poor-quality images and greater clarity of diagnostically important details in normalized slides demonstrate the potential of stain normalization in the routine practice of prostate cancer assessment.