scholarly journals piNET–An Automated Proliferation Index Calculator Framework for Ki67 Breast Cancer Images

Cancers ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 11
Author(s):  
Rokshana Stephny Geread ◽  
Abishika Sivanandarajah ◽  
Emily Rita Brouwer ◽  
Geoffrey A. Wood ◽  
Dimitrios Androutsos ◽  
...  
Keyword(s):  
Tumor Cells ◽  
Tissue Microarrays ◽  
False Positives ◽  
Proliferation Index ◽  
Tumor Segmentation ◽  
Significant Activity ◽  
Hotspot Analysis ◽  
Weak Supervision ◽  
Wide Variability ◽  
Whole Slide Images

In this work, a novel proliferation index (PI) calculator for Ki67 images called piNET is proposed. It is successfully tested on four datasets, from three scanners comprised of patches, tissue microarrays (TMAs) and whole slide images (WSI), representing a diverse multi-centre dataset for evaluating Ki67 quantification. Compared to state-of-the-art methods, piNET consistently performs the best over all datasets with an average PI difference of 5.603%, PI accuracy rate of 86% and correlation coefficient R = 0.927. The success of the system can be attributed to several innovations. Firstly, this tool is built based on deep learning, which can adapt to wide variability of medical images—and it was posed as a detection problem to mimic pathologists’ workflow which improves accuracy and efficiency. Secondly, the system is trained purely on tumor cells, which reduces false positives from non-tumor cells without needing the usual pre-requisite tumor segmentation step for Ki67 quantification. Thirdly, the concept of learning background regions through weak supervision is introduced, by providing the system with ideal and non-ideal (artifact) patches that further reduces false positives. Lastly, a novel hotspot analysis is proposed to allow automated methods to score patches from WSI that contain “significant” activity.

scholarly journals piNET: An Automated Proliferation Index Calculator Framework for Ki67 Breast Cancer Images

2020 ◽  
Author(s):  
Rokshana Stephny Geread ◽  
Abishika Sivanandarajah ◽  
Emily Brouwer ◽  
Geoffrey A. Wood ◽  
Dimitrios Androutsos ◽  
...  
Keyword(s):  
Tissue Microarrays ◽  
False Positives ◽  
Tumour Cells ◽  
Proliferation Index ◽  
Significant Activity ◽  
Hotspot Analysis ◽  
Weak Supervision ◽  
Tumour Segmentation ◽  
Wide Variability ◽  
Automated Methods

AbstractIn this work, a novel proliferation index (PI) calculator for Ki67 images called piNET is proposed. It is successfully tested on four datasets, from three scanners comprised of patches, tissue microarrays (TMAs) and wholeslide images (WSI), representing a diverse multicentre dataset for evaluating Ki67 quantification. Compared to state-of-the-art methods, piNET consistently performs the best over all datasets with an average PI difference of 5.603%, PI accuracy rate of 86% and correlation coefficient R = 0.927. The success of the system can be attributed to a number of innovations. Firstly, this tool is built based on deep learning, which can adapt to wide variability of medical images – and it was posed as a detection problem to mimic pathologists’ workflow which improves accuracy and efficiency. Secondly, the system is trained purely on tumour cells, which reduces false positives from non-tumour cells without needing the usual pre-requisite tumour segmentation step for Ki67 quantification. Thirdly, the concept of learning background regions through weak supervision is introduced, by providing the system with ideal and non-ideal (artifact) patches that further reduces false positives. Lastly, a novel hotspot analysis is proposed to allow automated methods to score patches from WSI that contain “significant” activity.

Histopathological and Clinical Differences Between Primary Breast Carcinomas With Neuroendocrine Features and Primary Breast Carcinomas Mimicking Neuroendocrine Features

2019 ◽  
Vol 27 (7) ◽  
pp. 744-752
Author(s):  
Canan Kelten Talu ◽  
Taha Cumhan Savli ◽  
Gulben Erdem Huq ◽  
Cem Leblebici
Keyword(s):  
Tumor Cells ◽  
Lymphovascular Invasion ◽  
Lymphocytic Infiltration ◽  
Growth Patterns ◽  
Proliferation Index ◽  
Positive Staining ◽  
High Grade ◽  
Breast Carcinomas ◽  
Ki 67 ◽  
Clear Cytoplasm

We aimed to determine the histopathological differences between primary breast carcinomas with neuroendocrine features (NEBC) and carcinomas mimicking neuroendocrine features (NEBC-like). Twenty-three cases with NEBC, all showing positive staining for synaptophysin and/or chromogranin-A in ≥50% of tumor cells and 36 cases with NEBC-like (no staining for neuroendocrine [NE] markers but suspicious for NE morphology in terms of solid/trabecular growth patterns) were included in the study. Significant differences were found between the groups in terms of the patients’ ages, histologic/nuclear grade of tumor, lymphovascular invasion, comedo-type ductal carcinoma in situ (DCIS), microcalcification, Ki-67 proliferation index, nuclear shape, and level of peritumoral lymphocytic infiltration. The presence of large-size solid cohesive groups of tumor cells; plasmocytoid, spindle, and/or columnar shapes of tumor cells; and eosinophilic-granular appearance of cytoplasm were mostly noted in the NEBC group. The presence of small- to medium-sized solid cohesive groups of tumor cells; high-grade histologic and nuclear features; clear cytoplasm; and round to ovoid nucleus were mostly noted in the NEBC-like group. No significant differences were found in terms of tumor size, ER/PR/HER2 status, as well as the presence of DCIS, elastosis, extracellular/intracellular mucin, signet ring cells, apocrine features, and accompanying papilloma or ductal ectasia. In conclusion, small- to medium-sized solid cohesive groups of tumor cells, high-grade features, clear cytoplasm, round to ovoid shape of nucleus, lymphovascular invasion, comedo-type DCIS, microcalcification, high level of Ki-67 proliferation index (≥20%), and moderate/strong level of peritumoral lymphocytic infiltration might support non-NE features in breast carcinomas.

scholarly journals A Machine-learning Approach for the Assessment of the Proliferative Compartment of Solid Tumors on Hematoxylin-Eosin-Stained Sections

Cancers ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1344 ◽  
Author(s):  
Francesco Martino ◽  
Silvia Varricchio ◽  
Daniela Russo ◽  
Francesco Merolla ◽  
Gennaro Ilardi ◽  
...  
Keyword(s):  
Machine Learning ◽  
Tumor Cells ◽  
Squamous Cell ◽  
Tissue Sample ◽  
Case Series ◽  
Tissue Microarrays ◽  
Fast Method ◽  
Tissue Samples ◽  
Promising Tool ◽  
Nuclear Features

We introduce a machine learning-based analysis to predict the immunohistochemical (IHC) labeling index for the cell proliferation marker Ki67/MIB1 on cancer tissues based on morphometrical features extracted from hematoxylin and eosin (H&E)-stained formalin-fixed, paraffin-embedded (FFPE) tumor tissue samples. We provided a proof-of-concept prediction of the Ki67/MIB1 IHC positivity of cancer cells through the definition and quantitation of single nuclear features. In the first instance, we set our digital framework on Ki67/MIB1-stained OSCC (oral squamous cell carcinoma) tissue sample whole slide images, using QuPath as a working platform and its integrated algorithms, and we built a classifier in order to distinguish tumor and stroma classes and, within them, Ki67-positive and Ki67-negative cells; then, we sorted the morphometric features of tumor cells related to their Ki67 IHC status. Among the evaluated features, nuclear hematoxylin mean optical density (NHMOD) presented as the best one to distinguish Ki67/MIB1 positive from negative cells. We confirmed our findings in a single-cell level analysis of H&E staining on Ki67-immunostained/H&E-decolored tissue samples. Finally, we tested our digital framework on a case series of oral squamous cell carcinomas (OSCC), arranged in tissue microarrays; we selected two consecutive sections of each OSCC FFPE TMA (tissue microarray) block, respectively stained with H&E and immuno-stained for Ki67/MIB1. We automatically detected tumor cells in H&E slides and generated a “false color map” (FCM) based on NHMOD through the QuPath measurements map tool. FCM nearly coincided with the actual immunohistochemical result, allowing the prediction of Ki67/MIB1 positive cells in a direct visual fashion. Our proposed approach provides the pathologist with a fast method of identifying the proliferating compartment of the tumor through a quantitative assessment of the nuclear features on H&E slides, readily appreciable by visual inspection. Although this technique needs to be fine-tuned and tested on larger series of tumors, the digital analysis approach appears to be a promising tool to quickly forecast the tumor’s proliferation fraction directly on routinely H&E-stained digital sections.

scholarly journals Bioinformatics analysis of whole slide images reveals significant neighborhood preferences of tumor cells in Hodgkin lymphoma

2020 ◽  
Vol 16 (1) ◽  
pp. e1007516
Author(s):  
Jennifer Hannig ◽  
Hendrik Schäfer ◽  
Jörg Ackermann ◽  
Marie Hebel ◽  
Tim Schäfer ◽  
...  
Keyword(s):  
Hodgkin Lymphoma ◽  
Tumor Cells ◽  
Bioinformatics Analysis ◽  
Neighborhood Preferences ◽  
Whole Slide Images

scholarly journals High SALM3 Expression in Tumor Cells and Fibroblasts Is Correlated with Poor Prognosis in Gastric Cancer Patients

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Ying Liu ◽  
Xiaoli Chen ◽  
Xi Chen ◽  
Xiaobing Yang ◽  
Qingjie Song ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Prognostic Factor ◽  
Tumor Cells ◽  
Survival Data ◽  
Proportional Hazards ◽  
Synapse Formation ◽  
Tissue Microarrays ◽  
Cox Proportional Hazards ◽  
Kaplan Meier ◽  
Kaplan Meier Plotter

Objective. The synaptic adhesion-like molecule (SALM) family is largely restricted to neural tissues and is involved in the regulation of neurite outgrowth and synapse formation. However, the expression of SALM3 in gastric cancer (GC) and its clinical significance remain unclear. The aim of the present study was to investigate the prognostic value of SALM3 in patients with GC.Patients and Methods. Expression of SALM3 was validated by tissue microarrays from 730 GC patients and statistically assessed for correlations with the clinical parameters and the prognosis of the patients. The transcriptional and survival data of SALM3 in GC patients were also mined through the Oncomine and Kaplan-Meier Plotter databases.Results. SALM3 is overexpressed in the tumor cells and fibroblasts of clinical GC tissues, and a high level of SALM3 was significantly associated with tumor invasive characteristics. Cox proportional hazards univariate and multivariate regression analyses revealed SALM3 expression in tumor cells or stroma as an independent prognostic factor in the overall survival rate of GC patients. Furthermore, the survival of GC patients with high SALM3 expression in both tumor cells and fibroblasts was significantly poorer than that of the other groups. Oncomine and Kaplan-Meier Plotter analyses further confirmed high levels of SALM3 expression in GC, and high levels of SALM3 expression were associated with shorter survival in patients.Conclusion. SALM3 may be a prognostic factor for GC and may potentially be a high-priority therapeutic target.

scholarly journals PL3.4 Intrinsic tumor plasticity in Glioblastoma allows for recreation of stem like-states and efficient tumor cell adaptation to new microenvironments

2019 ◽  
Vol 21 (Supplement_3) ◽  
pp. iii3-iii3
Author(s):  
A Golebiewska ◽  
A Dirkse ◽  
T Buder ◽  
Y A Yabo ◽  
S Poovathingal ◽  
...  
Keyword(s):  
Stem Cell ◽  
Tumor Cell ◽  
Tumor Cells ◽  
Environmental Conditions ◽  
Phenotypic Heterogeneity ◽  
Cellular Heterogeneity ◽  
Proliferation Index ◽  
State Transitions ◽  
Tumor Plasticity ◽  
Intrinsic Plasticity

Abstract BACKGROUND Cellular heterogeneity has been well established within numerous cancer types, including malignant brain tumours. Initially, cancer stem cells (CSC) have been accounted for formation of phenotypic heterogeneity and tumor progression in glioblastoma (GBM). Recent data, however, suggest that CSCs may not represent a stable entity and intrinsic plasticity plays a key role in tumor adaptation to changing microenvironments. The question arises whether CSCs are a defined subpopulation of tumor cells or whether they represent a changing entity that any cancer cell can adopt depending on the environmental conditions. MATERIAL AND METHODS Intra-tumoral phenotypic heterogeneity was interrogated at the single cell transcriptomic and proteomic level in GBM patient-derived orthotopic xenografts (PDOXs) and stem-like cultures. Tumor cell subpopulations were further classified based on expression of four stem cell-associated membrane markers (CD133, CD15, A2B5 and CD44). The resulting 16 subpopulations were FACS isolated and functionally analyzed. Mathematical Markov modelling was applied to calculate state transitions between cell states. RESULTS GBM patient biopsies, PDOXs and stem-like cell cultures display remarkable stem cell-associated intra-tumoral heterogeneity. Independent of marker expression, all analysed tumor subpopulations carried stem-cell properties and had the capacity to recreate phenotypic heterogeneity. Mathematical modeling revealed a different propensity in reforming the original heterogeneity over time, which was independent of the proliferation index but linked to tumorigenic potential. Although subpopulations varied in their potential to adapt to new environments, all were able to reach a steady state microenvironment-specific equilibrium. CONCLUSION Our results suggest that phenotypic heterogeneity in GBM results from intrinsic plasticity allowing tumor cells to effectively adapt to new microenvironments. Cellular states are non-hierarchical, reversible and occur via stochastic state transitions of existing populations, striving towards an equilibrium instructed by the microenvironment. Our data provides evidence that CSCs do not represent a clonal entity defined by distinct functional properties and transcriptomic signatures, but rather a cellular state that is determined by environmental conditions, which has implications for the design of treatment strategies targeting CSC-like states.

DNA cytometric parameters as prognostic factors of chemoimmunotherapy for ovarian cancer.

2017 ◽  
Vol 35 (15_suppl) ◽  
pp. e17041-e17041
Author(s):  
Galina Andreevna Nerodo ◽  
Oleg Ivanovich Kit ◽  
Inna Arnoldovna Novikova ◽  
Anna Ardzha ◽  
Ekaterina V. Verenikina ◽  
...  
Keyword(s):  
Cell Cycle ◽  
Ovarian Cancer ◽  
Prognostic Factors ◽  
Tumor Cells ◽  
Intraperitoneal Administration ◽  
Proliferation Index ◽  
Becton Dickinson ◽  
Dna Index ◽  
Group A ◽  
Group B

e17041 Background: The aim of the study was to analyze chemoimmunotherapy influence on DNA cytometric parameters in patients with ovarian cancer. Methods: The study included stage IIIC-IV ovarian cancer patients divided into two groups in dependence on the treatment: group A – 21 patients who did not receive neoadjuvant chemotherapy before surgery; group B – 24 patients who received neoadjuvant chemoimmunotherapy with intraperitoneal administration of interferon-gamma (ingaron) and the following surgery. The groups were identical in terms of the disease stages, age and general condition of patients. DNAs were analyzed in fresh tumor tissues using CycleTEST Plus DNA Reagent Kit (Becton Dickinson, USA). The data were processed using the ModFit LT program allowing analysis of ploidy and distribution of tumor cells in phases of the cell cycle. The proportion of cells with different DNA content in the histogram was calculated as a percentage of the total number of studied cells. Proliferation index (PI) was calculated as the total amount of tumor cells in S and G2+М phases of the cell cycle. Results: Full-volume surgery (panhysterectomy+omentectomy) was performed in 52.4% in group A and in 92% in group B, adnexectomy only – in 38.1% and 8%, respectively; exploratory surgeries were performed in group A only – 9.5%. During 2 years of the observation, recurrence in group A was found in 38% of patients, in group B – in 12.5%. Analysis of DNA cytometric parameters showed the minimal proliferation rates (percentage of cells in S phase) in group B – 12.6±2.3%, compared with 20.7±3.4% in group A. PI was lowest in group B – 13±2.7%, compared with group A – 22.4±3.3% (p≤0.05). Significant decrease in the DNA index by 1.3 times was noted in patients in group B in comparison with group A (1.11±0.01% and 1.4±0.05%, respectively) (p≤ 0.05). Conclusions: Thus,DNA cytometric parameters reflect the effectiveness of chemoimmunotherapy and can be used as prognostic factors.

scholarly journals Spatial heterogeneity and organization of tumor mutation burden and immune infiltrates within tumors based on whole slide images correlated with patient survival in bladder cancer

2019 ◽  
Author(s):  
Hongming Xu ◽  
Sunho Park ◽  
Jean René Clemenceau ◽  
Jinhwan Choi ◽  
Nathan Radakovich ◽  
...  
Keyword(s):  
Bladder Cancer ◽  
Spatial Heterogeneity ◽  
Tumor Cells ◽  
Tumor Infiltrating Lymphocytes ◽  
The Cancer Genome Atlas ◽  
Mutation Burden ◽  
Prognostic Utility ◽  
Cancer Genome Atlas ◽  
Infiltrating Lymphocytes ◽  
Whole Slide Images

AbstractHigh-TMB (TMB-H) could result in an increased number of neoepitopes from somatic mutations expressed by a patient’s own tumor cell which can be recognized and targeted by neighboring tumor-infiltrating lymphocytes (TILs). Deeper understanding of spatial heterogeneity and organization of tumor cells and their neighboring immune infiltrates within tumors could provide new insights into tumor progression and treatment response. Here we developed and applied computational approaches using digital whole slide images (WSIs) to investigate spatial heterogeneity and organization of regions harboring TMB-H tumor cells and TILs within tumors, and its prognostic utility. In experiments using WSIs from The Cancer Genome Atlas bladder cancer (BLCA), our findings show that WSI-based approaches can reliably predict patient-level TMB status and delineate spatial TMB heterogeneity and co-organization with TILs. TMB-H patients with low spatial heterogeneity enriched with high TILs show improved overall survival indicating a prognostic role of spatial TMB and TILs information in BLCA.

scholarly journals Performance of Different Diagnostic PD-L1 Clones in Head and Neck Squamous Cell Carcinoma

2021 ◽  
Vol 8 ◽  
Author(s):  
Julika Ribbat-Idel ◽  
Franz F. Dressler ◽  
Rosemarie Krupar ◽  
Christian Watermann ◽  
Finn-Ole Paulsen ◽  
...  
Keyword(s):  
Head And Neck ◽  
Tumor Cells ◽  
Squamous Cell ◽  
Tumor Tissue ◽  
Checkpoint Inhibitors ◽  
Principal Component ◽  
Tissue Microarrays ◽  
Daily Routine ◽  
Therapeutic Decision Making ◽  
Reported Data

Background: The approval of immune checkpoint inhibitors in combination with specific diagnostic biomarkers presents new challenges to pathologists as tumor tissue needs to be tested for expression of programmed death-ligand 1 (PD-L1) for a variety of indications. As there is currently no requirement to use companion diagnostic assays for PD-L1 testing in Germany different clones are used in daily routine. While the correlation of staining results has been tested in various entities, there is no data for head and neck squamous cell carcinomas (HNSCC) so far.Methods: We tested five different PD-L1 clones (SP263, SP142, E1L3N, 22-8, 22C3) on primary HNSCC tumor tissue of 75 patients in the form of tissue microarrays. Stainings of both immune and tumor cells were then assessed and quantified by pathologists to simulate real-world routine diagnostics. The results were analyzed descriptively and the resulting staining pattern across patients was further investigated by principal component analysis and non-negative matrix factorization clustering.Results: Percentages of positive immune and tumor cells varied greatly. Both the resulting combined positive score as well as the eligibility for certain checkpoint inhibitor regimens was therefore strongly dependent on the choice of the antibody. No relevant co-clustering and low similarity of relative staining patterns across patients was found for the different antibodies.Conclusions: Performance of different diagnostic anti PD-L1 antibody clones in HNSCC is less robust and interchangeable compared to reported data from other tumor entities. Determination of PD-L1 expression is critical for therapeutic decision making and may be aided by back-to-back testing of different PD-L1 clones.

scholarly journals Large B-cell Lymphoma with IRF4 rearrangement: a pitfall in the pathological diagnosis of lymphoma

2021 ◽  
Author(s):  
Minya Lu ◽  
Lisong Teng ◽  
Zhe Wang ◽  
Xiaodong Teng ◽  
Zhaoming Wang
Keyword(s):  
In Situ Hybridization ◽  
B Cell ◽  
Tumor Cells ◽  
Cell Lymphoma ◽  
B Cell Lymphoma ◽  
Growth Patterns ◽  
Proliferation Index ◽  
Large B Cell Lymphoma ◽  
Large B Cell

Abstract Background Large B-cell lymphoma (LBCL) with interferon regulatory factor 4 (IRF4) rearrangement (IRF4+LBCL) is a rare and newly discovered subtype of mature B cell neoplasms. Case presentationHere, we describe a patient of 32 years old who was diagnosed IRF4+LBCL. Histological examination showed the normal structure of the lymphoid tissues were destroyed, and slightly crowded follicular or nodal structures instead. There were obvious necrosis on the surface of tonsil and the central part of some follicles. The monomorphic atypical lymphoid cells proliferated and grew consistently, which were of medium size or large, and the nuclear chromatin was opening. Some tumor cells can be seen around the normal striated muscle tissues near the tonsils. Immunohistochemistry (IHC) could show that CD20, CD79a, MUM-1 and BCL6 were positive, Ki-67 was 80%; CD3, CD5, CD10, BCL2, CD30, CD56, CD99, CD38, and CD138 were negative. In situ hybridization (ISH) of EBER was negative. Fluorescence in situ hybridization (FISH) confirmed that IRF4 gene rearrangement was found in tumor cells. The patient was followed up for 18 months without tumor after chemotherapy. ConclusionGenerally speaking, destructive growth patterns with a large number of necrosis, high proliferation index and so on all suggest that the tumor is highly invasive. And in terms of pathological morphology, IRF4+LBCL can be similar to both high-grade follicular lymphoma (FL) and diffuse large B-cell lymphoma (DLBCL). But actually this disease is indolent and significantly different.

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