Digital Image Analysis of the Proliferation Markers Ki67 and Phosphohistone H3 in Gastroenteropancreatic Neuroendocrine Neoplasms: Accuracy of Grading Compared With Routine Manual Hot Spot Evaluation of the Ki67 Index

Assessment of Ki-67 Proliferative Index in Cytological Samples of Nodal B-Cell Lymphomas

BACKGROUND

The Ki-67 proliferative index (PI) is part of the diagnosis of nodal B-cell lymphoma (nBCL), but its determination in cytological samples is not standardized. We aimed to establish an approach for the accurate determination of the Ki-67 PI in cytological slides to differentiate between indolent and aggressive nBCLs.

METHODS

Patients diagnosed with nBCL by fine-needle aspiration biopsy and subsequent excision biopsy were included. Cell suspensions were prepared from biopsy samples for CD3/Ki-67 double immunocytochemical staining and flow-cytometric verification of lymphoma B-cell counts. The Ki-67 PI was assessed by manual counting and eyeballing in cytology and eyeballing in histology. The cut-off values for the differentiation between aggressive and indolent lymphomas were determined for each method.

RESULTS

A strong correlation between manual and flow-cytometric counting of lymphoma B cells was confirmed (interclass correlation coefficient (IC coef.) = 0.78). The correlation of the Ki-67 PI determined in cytological and histological slides was also strong (IC coef. > 0.80). Histologically, 55 cases were classified as indolent and 31 as aggressive nBCLs. KI-67 PI cut-off values of 28.5%, 27.5%, and 35.5% were established for manual counting and eyeballing in cytology and eyeballing in histology, respectively, with high sensitivity and specificity.

CONCLUSIONS

The Ki-67 PI, assessed by manual counting and eyeballing in cytological samples, accurately differentiates between indolent and aggressive nBCLs.

Comprehensive bioinformatics analysis of integrator complex subunits: expression patterns, immune infiltration, and prognostic signature, validated through experimental approaches in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is a common gastrointestinal malignancy with a high incidence and poor prognosis. The subunits of the integrator complex (INTS1-14) play a crucial role in regulating genes dependent on RNA Polymerase II, which may be associated with cancer. However, the role of INTSs in HCC remains unclear. This study aims to comprehensively analyze the clinical value and potential role of INTS family genes in HCC through systematic bioinformatics analysis.

METHODS

We employed various public databases, including UALCAN, HPA, Kaplan-Meier Plotter, GEPIA2, TNMplot, STRING, TIMER, and TISIDB, to investigate the expression levels, clinicopathological correlations, diagnostic and prognostic value, genetic alterations, co-expression network, molecular targets, and immune infiltration of INTSs in HCC. Additionally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) were utilized to investigate the biological functions of genes associated with INTSs. Furthermore, Western blot, real-time fluorescence quantitative reverse transcription polymerase chain reaction (RT-qPCR), and immunohistochemistry techniques were employed to assess the expression of relevant proteins and genes. The proliferation of HCC cells was evaluated using the CCK8 assay.

RESULTS

We found that in HCC, there was a significant upregulation of INTSs at the transcriptional level, particularly INTS1, INTS4, INTS7, and INTS8. Additionally, the protein levels of INTS1 and INTS8 were notably elevated. The overexpression of these INTSs was strongly correlated with tumor stages in HCC patients. INTS1, INTS4, INTS7, and INTS8 exhibited significant diagnostic and prognostic value in HCC. Moreover, their expression was associated with immune infiltrations and activated status, including B cells, CD8 + T cells, CD4 + T cells, NK cells, macrophages, and dendritic cells. Functional predictions indicated that INTS1, INTS4, INTS7, and INTS8 were involved in various cancer-related signaling pathways, such as TRAIL, IFN-gamma, mTOR, CDC42, Apoptosis, and the p53 pathway. Furthermore, we observed a significant upregulation of INTS1, INTS4, INTS7, and INTS8 expression in HCC cell lines compared to normal liver cell lines. The level of INTS1 protein was higher in cancerous tissues compared to adjacent non-cancerous tissues (n = 16), and the suppression of INTS1 resulted in a significant decrease in the proliferation of Huh7 cells.

CONCLUSION

These findings indicate the potential of INTS family genes as diagnostic biomarkers and therapeutic targets in HCC. Further research is needed to understand the underlying mechanisms and explore clinical applications.

Primary omental smooth muscle tumor in an adult male: a diagnostic dilemma for leiomyoma: a case report

BACKGROUND

The greater omentum comprises peritoneal, adipose, vascular, and lymphoid tissues. Most omental malignancies are metastatic tumors, and the incidence of primary tumors is rare. We report on a prior omental smooth muscle tumor case in an adult male patient.

CASE PRESENTATION

A 54-year-old Japanese male patient with no relevant medical history was diagnosed with an abdominal mass during a routine medical checkup. Subsequent contrast-enhanced computed tomography revealed a mass of approximately 3 cm in size in the greater omentum, and a laparotomy was performed. A 27 × 25 × 20 mm raised lesion was found in the omentum. Microscopically, spindle cells were observed and arranged in whorls and fascicles. Individual tumor cells had short spindle-shaped nuclei with slightly increased chromatin and were characterized by a slightly eosinophilic, spindle-shaped cytoplasm. The mitotic count was less than 1 per 50 high-power fields. The tumor cells showed positive immunoreactivity for α smooth muscle actin, HHF35, and desmin on immunohistochemical examination. The Ki-67 labeling index using the average method was 1.76% (261/14806). No immunoreactivity was observed for any of the other tested markers. We considered leiomyoma owing to a lack of malignant findings. However, primary omental leiomyoma has rarely been reported, and it can be difficult to completely rule out the malignant potential of smooth muscle tumors in soft tissues. Our patient was decisively diagnosed with a primary omental smooth muscle tumor considering leiomyoma. Consequently, the patient did not undergo additional adjuvant therapy and was followed up. The patient was satisfied with treatment and showed neither recurrence nor metastasis at the 13-month postoperative follow-up.

DISCUSSION AND CONCLUSION

We encountered a primary smooth muscle tumor of the greater omentum with no histological findings suggestive of malignancy in an adult male patient. However, omental smooth muscle tumors are extremely difficult to define as benign, requiring careful diagnosis. Further case reports with long-term follow-up and case series are required to determine whether a true omental benign smooth muscle tumor (leiomyoma) exists. In addition, proper interpretation of the Ki-67 labeling index should be established. This case study is a foundation for future research.

Updates on lung neuroendocrine neoplasm classification

Lung neuroendocrine neoplasms (NENs) are a heterogeneous group of pulmonary neoplasms showing different morphological patterns and clinical and biological characteristics. The World Health Organisation (WHO) classification of lung NENs has been recently updated as part of the broader attempt to uniform the classification of NENs. This much-needed update has come at a time when insights from seminal molecular characterisation studies revolutionised our understanding of the biological and pathological architecture of lung NENs, paving the way for the development of novel diagnostic techniques, prognostic factors and therapeutic approaches. In this challenging and rapidly evolving landscape, the relevance of the 2021 WHO classification has been recently questioned, particularly in terms of its morphology-orientated approach and its prognostic implications. Here, we provide a state-of-the-art review on the contemporary understanding of pulmonary NEN morphology and the potential contribution of artificial intelligence, the advances in NEN molecular profiling with their impact on the classification system and, finally, the key current and upcoming prognostic factors.

Serum Metabolite Biomarkers for Pancreatic Tumors: Neuroendocrine and Pancreatic Ductal Adenocarcinomas-A Preliminary Study

BACKGROUND

Pancreatic cancer is the most common pancreatic solid malignancy with an aggressive clinical course and low survival rate. There are a limited number of reliable prognostic biomarkers and a need to understand the pathogenesis of pancreatic tumors; neuroendocrine (PNET) and pancreatic ductal adenocarcinomas (PDAC) encouraged us to analyze the serum metabolome of pancreatic tumors and disturbances in the metabolism of PDAC and PNET.

METHODS

Using the AbsoluteIDQ^® p180 kit (Biocrates Life Sciences AG, Innsbruck, Austria) with liquid chromatography-mass spectrometry (LC-MS), we identified changes in metabolite profiles and disrupted metabolic pathways serum of NET and PDAC patients.

RESULTS

The concentration of six metabolites showed statistically significant differences between the control group and PDAC patients (p.adj < 0.05). Glutamine (Gln), acetylcarnitine (C2), and citrulline (Cit) presented a lower concentration in the serum of PDAC patients, while phosphatidylcholine aa C32:0 (PC aa C32:0), sphingomyelin C26:1 (SM C26:1), and glutamic acid (Glu) achieved higher concentrations compared to serum samples from healthy individuals. Five of the tested metabolites: C2 (FC = 8.67), and serotonin (FC = 2.68) reached higher concentration values in the PNET serum samples compared to PDAC, while phosphatidylcholine aa C34:1 (PC aa C34:1) (FC = -1.46 (0.68)) had a higher concentration in the PDAC samples. The area under the curves (AUC) of the receiver operating characteristic (ROC) curves presented diagnostic power to discriminate pancreatic tumor patients, which were highest for acylcarnitines: C2 with AUC = 0.93, serotonin with AUC = 0.85, and PC aa C34:1 with AUC = 0.86.

CONCLUSIONS

The observations presented provide better insight into the metabolism of pancreatic tumors, and improve the diagnosis and classification of tumors. Serum-circulating metabolites can be easily monitored without invasive procedures and show the present clinical patients' condition, helping with pharmacological treatment or dietary strategies.

Artificial intelligence in liver cancers: Decoding the impact of machine learning models in clinical diagnosis of primary liver cancers and liver cancer metastases

Liver cancers are the fourth leading cause of cancer-related mortality worldwide. In the past decade, breakthroughs in the field of artificial intelligence (AI) have inspired development of algorithms in the cancer setting. A growing body of recent studies have evaluated machine learning (ML) and deep learning (DL) algorithms for pre-screening, diagnosis and management of liver cancer patients through diagnostic image analysis, biomarker discovery and predicting personalized clinical outcomes. Despite the promise of these early AI tools, there is a significant need to explain the 'black box' of AI and work towards deployment to enable ultimate clinical translatability. Certain emerging fields such as RNA nanomedicine for targeted liver cancer therapy may also benefit from application of AI, specifically in nano-formulation research and development given that they are still largely reliant on lengthy trial-and-error experiments. In this paper, we put forward the current landscape of AI in liver cancers along with the challenges of AI in liver cancer diagnosis and management. Finally, we have discussed the future perspectives of AI application in liver cancer and how a multidisciplinary approach using AI in nanomedicine could accelerate the transition of personalized liver cancer medicine from bench side to the clinic.

All Together Now: Standardization of Nomenclature for Neuroendocrine Neoplasms across Multiple Organs

Neuroendocrine neoplasms (NENs) span virtually all organ systems and exhibit a broad spectrum of behavior, from indolent to highly aggressive. Historically, nomenclature and grading practices have varied widely across, and even within, organ systems. However, certain core features are recapitulated across anatomic sites, including characteristic morphology and the crucial role of proliferative activity in prognostication. A recent emphasis on unifying themes has driven an increasingly standardized approach to NEN classification, as delineated in the World Health Organization's Classification of Tumours series. Here, we review recent developments in NEN classification, with a focus on NENs of the pancreas and lungs.

Update on C-Cell Neuroendocrine Neoplasm: Prognostic and Predictive Histopathologic and Molecular Features of Medullary Thyroid Carcinoma

Medullary thyroid carcinoma (MTC) is a C-cell-derived epithelial neuroendocrine neoplasm. With the exception of rare examples, most are well-differentiated epithelial neuroendocrine neoplasms (also known as neuroendocrine tumors in the taxonomy of the International Agency for Research on Cancer [IARC] of the World Health Organization [WHO]). This review provides an overview and recent evidence-based data on the molecular genetics, disease risk stratification based on clinicopathologic variables including molecular profiling and histopathologic variables, and targeted molecular therapies in patients with advanced MTC. While MTC is not the only neuroendocrine neoplasm in the thyroid gland, other neuroendocrine neoplasms in the thyroid include intrathyroidal thymic neuroendocrine neoplasms, intrathyroidal parathyroid neoplasms, and primary thyroid paragangliomas as well as metastatic neuroendocrine neoplasms. Therefore, the first responsibility of a pathologist is to distinguish MTC from other mimics using appropriate biomarkers. The second responsibility includes meticulous assessment of the status of angioinvasion (defined as tumor cells invading through a vessel wall and forming tumor-fibrin complexes, or intravascular tumor cells admixed with fibrin/thrombus), tumor necrosis, proliferative rate (mitotic count and Ki67 labeling index), and tumor grade (low- or high-grade) along with the tumor stage and the resection margins. Given the morphologic and proliferative heterogeneity in these neoplasms, an exhaustive sampling is strongly recommended. Routine molecular testing for pathogenic germline RET variants is typically performed in all patients with a diagnosis of MTC; however, multifocal C-cell hyperplasia in association with at least a single focus of MTC and/or multifocal C-cell neoplasia are morphological harbingers of germline RET alterations. It is of interest to assess the status of pathogenic molecular alterations involving genes other than RET like the MET variants in MTC families with no pathogenic germline RET variants. Furthermore, the status of somatic RET alterations should be determined in all advanced/progressive or metastatic diseases, especially when selective RET inhibitor therapy (e.g., selpercatinib or pralsetinib) is considered. While the role of routine SSTR2/5 immunohistochemistry remains to be further clarified, evidence suggests that patients with somatostatin receptor (SSTR)-avid metastatic disease may also benefit from the option of ¹⁷⁷Lu-DOTATATE peptide radionuclide receptor therapy. Finally, the authors of this review make a call to support the nomenclature change of MTC to C-cell neuroendocrine neoplasm to align this entity with the IARC/WHO taxonomy since MTCs represent epithelial neuroendocrine neoplasms of endoderm-derived C-cells.

Digital Image Analysis of Ki67 Heterogeneity Improves the Diagnosis and Prognosis of Gastroenteropancreatic Neuroendocrine Neoplasms

Ki67 is a reliable grading and prognostic biomarker of gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs). The intratumor heterogeneity of Ki67, correlated with tumor progression, is a valuable factor that requires image analysis. The application of digital image analysis (DIA) enables new approaches for the assessment of Ki67 heterogeneity distribution. We investigated the diagnostic utility of Ki67 heterogeneity parameters in the classification and grading of GEP-NENs and explored their clinical values with regard to their prognostic relevance. The DIA algorithm was performed on whole-slide images of 102 resection samples with Ki67 staining. Good agreement was observed between the manual and DIA methods in the hotspot evaluation (R² = 0.94, P < .01). Using the grid-based region of interest approach, score-based heat maps provided a distinctive overview of the intratumoral distribution of Ki67 between neuroendocrine carcinomas and neuroendocrine tumors. The computation of heterogeneity parameters related to DIA-determined Ki67 showed that the coefficient of variation and Morisita-Horn index were directly related to the classification and grading of GEP-NENs and provided insights into distinguishing high-grade neuroendocrine neoplasms (grade 3 neuroendocrine tumor vs neuroendocrine carcinoma, P < .01). Our study showed that a high Morisita-Horn index correlated with poor disease-free survival (multivariate analysis: hazard ratio, 56.69), which was found to be the only independent predictor of disease-free survival in patients with GEP-NEN. These spatial biomarkers have an impact on the classification and grading of tumors and highlight the prognostic associations of tumor heterogeneity. Digitization of Ki67 variations provides a direct and objective measurement of tumor heterogeneity and better predicts the biological behavior of GEP-NENs.

The value of Phosphohistone H3 as a cell proliferation marker in oral squamous cell carcinoma. A comparative study with Ki-67 and the mitotic activity index

BACKGROUND

The Phosphohistone H3 (PHH3) antibody is recognized as a biomarker of cell proliferation, specific for cells in mitosis, of prognostic value in different malignant neoplasms, however it has been poorly studied in oral squamous cell carcinoma (OSCC). The main objective of this study was to evaluate the immunoexpression of the PHH3 in the OSCC, through the correlation with the immunoexpression of Ki-67, the mitotic activity index (MAI), histological grading, clinical-morphological parameters and the rate of survival.

MATERIAL AND METHODS

The study sample consisted of 62 cases of OSCC diagnosed in the Pathological Anatomy Laboratory of the Faculty of Dentistry, University of the Republic (Uruguay). In each of them, an immunohistochemical technique was performed for Ki-67 and PHH3 (serine 10) antibodies. Image J software was used for the MAI and biomarker quantification, defining the percentage of positivity and mitotic figures per 1000 tumor cells.

RESULTS

a significant association was obtained between the expression of PHH3 (p 0.016) and MAI (p 0.031) with survival time. However, no similar relationship was found with Ki-67 (p 0.295). Although it was confirmed a statistical association between histological grade and Ki-67 immunoexpression (p 0.004), PHH3 did not show a similar relationship (p 0.564).

CONCLUSIONS

It was confirmed the role of the PHH3 antibody as a biomarker of mitotic figures in OSCC and as a potential marker of cell proliferation. It is noteworthy that this is one of the first works that evaluates a possible relationship between the expression of this antibody and survival in OSCC.

Ki-67 assessment of pancreatic neuroendocrine neoplasms: Systematic review and meta-analysis of manual vs. digital pathology scoring

Ki-67 assessment is a key step in the diagnosis of neuroendocrine neoplasms (NENs) from all anatomic locations. Several challenges exist related to quantifying the Ki-67 proliferation index due to lack of method standardization and inter-reader variability. The application of digital pathology coupled with machine learning has been shown to be highly accurate and reproducible for the evaluation of Ki-67 in NENs. We systematically reviewed all published studies on the subject of Ki-67 assessment in pancreatic NENs (PanNENs) employing digital image analysis (DIA). The most common advantages of DIA were improvement in the standardization and reliability of Ki-67 evaluation, as well as its speed and practicality, compared to the current gold standard approach of manual counts from captured images, which is cumbersome and time consuming. The main limitations were attributed to higher costs, lack of widespread availability (as of yet), operator qualification and training issues (if it is not done by pathologists), and most importantly, the drawback of image algorithms counting contaminating non-neoplastic cells and other signals like hemosiderin. However, solutions are rapidly developing for all of these challenging issues. A comparative meta-analysis for DIA versus manual counting shows very high concordance (global coefficient of concordance: 0.94, 95% CI: 0.83-0.98) between these two modalities. These findings support the widespread adoption of validated DIA methods for Ki-67 assessment in PanNENs, provided that measures are in place to ensure counting of only tumor cells either by software modifications or education of non-pathologist operators, as well as selection of standard regions of interest for analysis. NENs, being cellular and monotonous neoplasms, are naturally more amenable to Ki-67 assessment. However, lessons of this review may be applicable to other neoplasms where proliferation activity has become an integral part of theranostic evaluation including breast, brain, and hematolymphoid neoplasms.

Visual and digital assessment of Ki-67 in breast cancer tissue - a comparison of methods

Background

In breast cancer (BC) Ki-67 cut-off levels, counting methods and inter- and intraobserver variation are still unresolved. To reduce inter-laboratory differences, it has been proposed that cut-off levels for Ki-67 should be determined based on the in-house median of 500 counted tumour cell nuclei. Digital image analysis (DIA) has been proposed as a means to standardize assessment of Ki-67 staining in tumour tissue. In this study we compared digital and visual assessment (VA) of Ki-67 protein expression levels in full-face sections from a consecutive series of BCs. The aim was to identify the number of tumour cells necessary to count in order to reflect the growth potential of a given tumour in both methods, as measured by tumour grade, mitotic count and patient outcome.

Methods

A series of whole sections from 248 invasive carcinomas of no special type were immunohistochemically stained for Ki-67 and then assessed by VA and DIA. Five 100-cell increments were counted in hot spot areas using both VA and DIA. The median numbers of Ki-67 positive tumour cells were used to calculate cut-off levels for Low, Intermediate and High Ki-67 protein expression in both methods.

Results

We found that the percentage of Ki-67 positive tumour cells was higher in DIA compared to VA (medians after 500 tumour cells counted were 22.3% for VA and 30% for DIA). While the median Ki-67% values remained largely unchanged across the 100-cell increments for VA, median values were highest in the first 1-200 cells counted using DIA. We also found that the DIA100 High group identified the largest proportion of histopathological grade 3 tumours 70/101 (69.3%).

Conclusions

We show that assessment of Ki-67 in breast tumours using DIA identifies a greater proportion of cases with high Ki-67 levels compared to VA of the same tumours. Furthermore, we show that diagnostic cut-off levels should be calibrated appropriately on the introduction of new methodology.

Quantitative Assessment of Epithelial Proliferation in Rat Mammary Gland Using Artificial Intelligence Independent of Choice of Proliferation Marker

Epithelial proliferation in the rat mammary gland is recommended in regulatory guidelines as an endpoint for assessment of the in vivo carcinogenic potential of insulin analogues. Epithelial proliferation is traditionally assessed by immunohistochemical staining of a proliferation marker, for example, 5-bromo-2'-deoxyuridine (BrdU) or Ki67, followed by labor-intensive manual counting of positive and negative cells. The aim of this study was to develop and validate an approach for image analysis based on artificial intelligence, which can be used for quantification of proliferation in rat mammary gland, independent of the choice of proliferation marker. Furthermore, the aim was to compare the markers BrdU, Ki67, and phosphorylated histone H3 (PHH3). A sequence of image analysis applications were developed, which allowed for quantification of proliferative activity in the mammary gland epithelium. These endpoints agreed well with manually counted labeling indices, with correlation coefficients in the range ≈0.92-0.93. In addition, all three proliferation markers were significantly correlated and could detect the variation in epithelial proliferation during the estrous cycle. In conclusion, image analysis can be used to quantify epithelial proliferation in the rat mammary gland and thereby replace time-consuming manual counting. Furthermore, BrdU, Ki67, and PHH3 can be used interchangeably to assess proliferation.