Real-world biomarker testing patterns: clinical-pathological portrait of early and late non-small cell lung cancer in hub and spoke North Italian centers.

Background: Lung cancer is still the main cause of cancer death. In the last decades, significant innovations were introduced in non-small cell lung cancer (NSCLC) treatment and management improving patient outcomes. The discovery of immune checkpoint inhibitors and the detection of an increasing list of actionable genetic alterations are enabling a tailored approach. Herein, we assessed in a pragmatic retrospective study the rate of biomarker tests within a large pulmonary pathology-based unit (PPU) network of the Veneto region (Northern Italy). Methods: Each PPU of 7 hubs and spoke centers implemented a biomarker database with pathologic and clinical data of patients with NSCLC diagnosis over 24 months. Results: Out of 1,817 NSCLC cases, 51% were advanced and 49% early stage, with 72% being adenocarcinomas. Programmed death ligand 1 expression and epidermal growth factor receptor mutations were available in most samples, 91% and 78%, respectively. Only 36% of advanced stages received all 5 biomarker tests with an increased rate over time. Co-occurring molecular alterations were detected in 42 cases (2%): the prevalence was (n=17) 41% and (n=25) 59% in early and late-stage adenocarcinomas, respectively. Conclusions: In this real-world study, while most patients received at least one biomarker test, less than 50% had all 5 biomarkers. The screening appeared to increase over time especially with the progressive use of next generation sequencing. Our results confirm the importance of systematic biomarker testing including all NSCLCs based on the evidence of several genomic alterations also in early-stage disease whose analysis may become relevant as neo-adjuvant targeted therapies are available. Keywords: Non-small cell lung cancer (NSCLC); biomarkers; actionable targets; lung cancer.

Comparison of Primary and Metastatic Fumarate Hydratase-Deficient Renal Cell Carcinomas Documents Morphologic Divergence and Potential Diagnostic Pitfall With Peritoneal Mesothelioma.

Fumarate hydratase (FH)-deficient renal cell carcinomas are rare neoplasms characterized by wide morphologic heterogeneity and pathogenetic mutations in the FH gene. They often show aggressive behavior with rapid diffusion to distant organs, so novel therapeutic scenarios have been explored, including EGFR inhibitors and PD-L1 expression for targeted immunotherapy. Herein, we investigated a series of 11 primary FH-deficient renal cell carcinomas and 7 distant metastases to evaluate tumor heterogeneity even in metastatic sites and estimate the specific spread rates to various organs. Furthermore, the tumors were tested for immunohistochemical PD-L1 expression and EGFR mutations. Most metastatic cases involved the abdominal lymph nodes (4/7; 57%), followed by the peritoneum (3/7; 42%), the liver (2/7; 29%), and the lungs (1/7; 14%). Six metastatic localizations were histologically documented, revealing a morphologic heterogeneous architecture often differing from that of the corresponding primary renal tumor. Peritoneal involvement morphologically resembled a benign reactive mesothelial process or primary peritoneal mesothelioma, thus advocating to perform an accurate immunohistochemical panel, including PAX8 and FH, to reach a proper diagnosis. A pure low-grade succinate dehydrogenase-looking primary FH-deficient renal cell carcinoma was also recorded. As for therapy, significant PD-L1 labeling was found in 60% of primary renal tumors, whereas none of them carried pathogenetic EGFR mutations. Our data show that FH-deficient renal cell carcinoma may be morphologically heterogeneous in metastases as well, which involve the lymph nodes, the liver, and the peritoneum more frequently than other renal tumors. Due to the high frequency of this latter (42%), pathologists should always be concerned about ruling out mesothelial-derived mimickers, and the occurrence of rarer, primary, low-grade-looking types. Finally, contrary to EGFR mutations, PD-L1 expression could be a possible predictive biomarker for the therapy of these tumors.

Digital pathology structure and deployment in Veneto: a proof-of-concept study.

Nowadays pathology laboratories are worldwide facing a digital revolution, with an increasing number of institutions adopting digital pathology (DP) and whole slide imaging solutions. Despite indeed providing novel and helpful advantages, embracing a whole DP workflow is still challenging, especially for wide healthcare networks. The Azienda Zero of the Veneto Italian region has begun a process of a fully digital transformation of an integrated network of 12 hospitals producing nearly 3 million slides per year. In the present article, we describe the planning stages and the operative phases needed to support such a disruptive transition, along with the initial preliminary results emerging from the project. The ultimate goal of the DP program in the Veneto Italian region is to improve patients' clinical care through a safe and standardized process, encompassing a total digital management of pathology samples, easy file sharing with experienced colleagues, and automatic support by artificial intelligence tools.

Benchmarking digital displays (monitors) for histological diagnoses: the nephropathology use case.

AIM: The digital transformation of the pathology laboratory is being continuously sustained by the introduction of innovative technologies promoting whole slide image (WSI)-based primary diagnosis. Here, we proposed a real-life benchmark of a pathology-dedicated medical monitor for the primary diagnosis of renal biopsies, evaluating the concordance between the 'traditional' microscope and commercial monitors using WSI from different scanners. METHODS: The College of American Pathologists WSI validation guidelines were used on 60 consecutive renal biopsies from three scanners (Aperio, 3DHISTECH and Hamamatsu) using pathology-dedicated medical grade (MG), professional grade (PG) and consumer-off-the-shelf (COTS) monitors, comparing results with the microscope diagnosis after a 2-week washout period. RESULTS: MG monitor was faster (1090 vs 1159 vs 1181 min, delta of 6-8%, p<0.01), with slightly better performances on the detection of concurrent diseases compared with COTS (κ=1 vs 0.96, 95% CI=0.87 to 1), but equal concordance to the commercial monitors on main diagnosis (κ=1). Minor discrepancies were noted on specific scores/classifications, with MG and PG monitors closer to the reference report (r=0.98, 95% CI=0.83 to 1 vs 0.98, 95% CI=0.83 to 1 vs 0.91, 95% CI=0.76 to 1, κ=0.93, 95% CI=077 to 1 vs 0.93, 95% CI=0.77 to 1 vs 0.86, 95% CI=0.64 to 1, κ=1 vs 0.50, 95% CI=0 to 1 vs 0.50, 95% CI=0 to 1, for IgA, antineutrophilic cytoplasmic antibody and lupus nephritis, respectively). Streamlined Pipeline for Amyloid detection through congo red fluorescence Digital Analysis detected amyloidosis on both monitors (4 of 30, 13% cases), allowing detection of minimal interstitial deposits with slight overestimation of the Amyloid Score (average 6 vs 7). CONCLUSIONS: The digital transformation needs careful assessment of the hardware component to support a smart and safe diagnostic process. Choosing the display for WSI is critical in the process and requires adequate planning.

The Puzzle of Preimplantation Kidney Biopsy Decision-Making Process: The Pathologist Perspective.

Kidney transplantation is the best treatment for end-stage renal disease since it offers the greatest survival benefit compared to dialysis. The gap between the number of renal transplants performed and the number of patients awaiting renal transplants leads to a steadily increasing pressure on the scientific community. Kidney preimplantation biopsy is used as a component of the evaluation of organ quality before acceptance for transplantation. However, the reliability and predictive value of biopsy data are controversial. Most of the previously proposed predictive models were not associated with graft survival, but what has to be reaffirmed is that histologic examination of kidney tissue can provide an objective window on the state of the organ that cannot be deduced from clinical records and renal functional studies. The balance of evidence indicates that reliable decisions about donor suitability must be made based on the overall picture. This work discusses recent trends that can reduce diagnostic timing and variability among players in the decision-making process that lead to kidney transplants, from the pathologist's perspective.

Artificial intelligence-based algorithms for the diagnosis of prostate cancer: A systematic review.

OBJECTIVES: The high incidence of prostate cancer causes prostatic samples to significantly affect pathology laboratories workflow and turnaround times (TATs). Whole-slide imaging (WSI) and artificial intelligence (AI) have both gained approval for primary diagnosis in prostate pathology, providing physicians with novel tools for their daily routine. METHODS: A systematic review according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines was carried out in electronic databases to gather the available evidence on the application of AI-based algorithms to prostate cancer. RESULTS: Of 6290 articles, 80 were included, mostly (59%) dealing with biopsy specimens. Glass slides were digitized to WSI in most studies (89%), roughly two-thirds of which (66%) exploited convolutional neural networks for computational analysis. The algorithms achieved good to excellent results about cancer detection and grading, along with significantly reduced TATs. Furthermore, several studies showed a relevant correlation between AI-identified histologic features and prognostic predictive variables such as biochemical recurrence, extraprostatic extension, perineural invasion, and disease-free survival. CONCLUSIONS: The published evidence suggests that AI can be reliably used for prostate cancer detection and grading, assisting pathologists in the time-consuming screening of slides. Further technologic improvement would help widening AI's adoption in prostate pathology, as well as expanding its prognostic predictive potential.

Discordance of Biomarker Expression Profile between Primary Breast Cancer and Synchronous Axillary Lymph Node Metastasis in Preoperative Core Needle Biopsy.

Background: Breast cancer (BC) is a heterogeneous disease made up of clones with different metastatic potential. Intratumoral heterogeneity may cause metastases to show divergent biomarker expression, potentially affecting chemotherapy response. Methods: We investigated the immunohistochemical (IHC) and FISH profile of estrogen receptors (ER), progesterone (PR) receptors, Ki67, and HER2 in a series of BC-matched primary tumors (PTs) and axillary lymph node (ALN) metastases in pre-operative core needle biopsies (CNBs). Phenotypical findings were correlated to morphological features and their clinical implications. Results: Divergent expression between PTs and ALNs was found in 10% of the tumors, often involving multiple biomarkers (12/31, 39%). Most (52%) displayed significant differences in ER and PR staining. HER2 divergences were observed in almost three-quarters of the cases (23/31, 74%), with five (16%) switching from negativity to overexpression/amplification in ALNs. Roughly 90% of disparities reflected significant morphological differences between PTs and ALN metastases. Less than half of the discrepancies (12/31, 39%) modified pre/post-operative treatment options. Conclusions: We observed relevant discrepancies in biomarker expression between PTs and metastatic ALNs in a noteworthy proportion (10%) of preoperative BC CNBs, which were often able to influence therapies. Hence, our data suggest routine preoperative assessment of biomarkers in both PTs and ALNs in cases showing significant morphological differences.

Natural Language Processing to extract SNOMED-CT codes from pathological reports.

Objective: The use of standardized structured reports (SSR) and suitable terminologies like SNOMED-CT can enhance data retrieval and analysis, fostering large-scale studies and collaboration. However, the still large prevalence of narrative reports in our laboratories warrants alternative and automated labeling approaches. In this project, natural language processing (NLP) methods were used to associate SNOMED-CT codes to structured and unstructured reports from an Italian Digital Pathology Department. Methods: Two NLP-based automatic coding systems (support vector machine, SVM, and long-short term memory, LSTM) were trained and applied to a series of narrative reports. Results: The 1163 cases were tested with both algorithms, showing good performances in terms of accuracy, precision, recall, and F1 score, with SVM showing slightly better performances as compared to LSTM (0.84, 0.87, 0.83, 0.82 vs 0.83, 0.85, 0.83, 0.82, respectively). The integration of an explainability allowed identification of terms and groups of words of importance, enabling fine-tuning, balancing semantic meaning and model performance. Conclusions: AI tools allow the automatic SNOMED-CT labeling of the pathology archives, providing a retrospective fix to the large lack of organization of narrative reports.