Comparation of the quantification of the proliferative index KI67 between eyeball and semi-automated digital analysis in gastro-intestinal neuroendrocrine tumors
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Abstract Background Neuroendocrine tumors (NETs) constitute tumors widely distributed and with heterogeneous biological behavior. For gastrointestinal neuroendocrine tumors (GI-NETs) the following prognostic factors have been identified: location, production of hormones, size and proliferative grade. The latter must be calculated using proliferation index by the number of mitosis or the proportion of tumor cells positive for Ki67 immunostaining. The objective of this study was to use a quantitative tool to calculate the Ki67 index in GI-NETs. Material and methods We reviewed 40 cases of GI-NETs diagnosed at the Department of Pathological Sciences, Santa Casa de Misericórdia de São Paulo Hospital between 2004 and 2014 and compared the Ki67 index by manual count using scanned photomicrographs with semi-automated digital analysis (MC) and eyeball estimation (EE) of the histological slide. After Ki67 immunostaining, the slides were scanned with 3DHistech Pannoramic Scanners. Hot spots were selected and exported in a high-resolution image format and the Ki67 index was calculated with semi-automated image analysis software (AxioVision 3.0). Ki67 immunoreactivity was expressed as the percentage of tumor cells with nuclear staining (number of positive tumor cells/a minimum of 500 total tumor cells). Results We compared the classification of the neuroendocrine tumor by using the two methods in the semi-automated method 26 maintained the same grade, while 14 were re-classified, 4 being upgraded and 10 downgraded. Conclusion In the EE method there was a larger estimate of the percentage of positivity for KI67. As the Ki67 values are the criteria for the classification of neuroendocrine tumors, the semi-automated method can have less error.Keywords:
Digital Image Analysis
Immunostaining
Ki-67
Proliferation index
Mitotic index
Automated method
Image Analysis
Proliferative index
Many studies have emphasised the importance of Ki-67 labeling index (LI) as the proliferation marker in meningiomas. Several authors confirmed, that Ki-67 LI has prognostic significance and correlates with likelihood of tumour recurrences. These observations were widely accepted by pathologists, but up till now no standard method for Ki-67 LI assessment was developed and introduced for the diagnostic pathology. In this paper we present a new computerised system for automated Ki-67 LI estimation in meningiomas as an aid for histological grading of meningiomas and potential standard method of Ki-67 LI assessment. We also discuss the concordance of Ki-67 LI results obtained by presented computerized system and expert pathologist, as well as possible pitfalls and mistakes in automated counting of immunopositive or negative cells. For the quantitative evaluation of digital images of meningiomas the designed software uses an algorithm based on mathematical description of cell morphology. This solution acts together with the Support Vector Machine (SVM) used in the classification mode for the recognition of immunoreactivity of cells. The applied sequential thresholding simulated well the human process of cell recognition. The same digital images of randomly selected tumour areas were parallelly analysed by computer and blindly by two expert pathologists. Ki-67 labeling indices were estimated and the results compared. The mean relative discrepancy between the levels of Ki-67 LI by our system and by the human expert did not exceed 14% in all investigated cases. These preliminary results suggest that the designed software could be an useful tool supporting the diagnostic digital pathology. However, more extended studies are needed for approval of this suggestion.
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Ki-67 is a nuclear protein that can be produced during cell proliferation. The Ki67 index is a valuable prognostic variable in several kinds of cancer. In breast cancer, the index is even routinely checked in many patients. Currently, pathologists use the immunohistochemistry method to calculate the percentage of Ki-67 positive malignant cells as Ki-67 index. The higher score usually means more aggressive tumor behavior. In clinical practice, the measurement of Ki-67 index relies on visual identifying method and manual counting. However, visual and manual assessment method is timeconsuming and leads to poor reproducibility because of different scoring standards or limited tumor area under assessment. Here, we use digital image processing technics including image binarization and image morphological operations to create a digital image analysis method to interpretate Ki-67 index. Then, 10 breast cancer specimens are used as validation with high accuracy (correlation efficiency r = 0.95127). With the assistance of digital image analysis, pathologists can interpretate the Ki67 index more efficiently, precisely with excellent reproducibility.
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The most common NHL subtype in SEEU is DLBCL (39%), and it manifests with a variety of cellular morphologies and a high proliferation index. Also, the GI tract is the most common site of extranodal NHLs, and most NHLs involving the GI tract are of B-cell lineage, of which diffuse large B-cell lymphoma is the most common subtype, irrespective of location. The last few years have seen digital pathology as a vital technology that has a positive impact on diagnostics, but studies on the use of DP for lymphoma identification, however, are still restricted to only determining whether a tumor is present or absent. Using the example of cases of malignant NHL, we aim to investigate the diagnostic utility of DP using QuPath software in evaluating the proliferation index and the prognostic significance and to show that improved visualization and analysis contribute to the convergence of these complementary diagnostic modalities for lymphomas. The average proliferation index (Ki67) was 58.33% with values between 10% and 85%. After the stratification of the cases, an increased proliferation index was observed in the majority of cases (53.33%), and this aspect was associated with the advanced age of the patients (p = 0.045). Visual assessment provides lower Ki67 values than automated digital image analysis. However, the agreement coefficient between the conventional method and the AI method indicates an excellent level of reliability (ICC1-0.970, ICC2-0.990). The multivariate analysis revealed that in the cases where the proliferation index Ki67 is high (˃70%), the IPI score represents an important risk factor predicting mortality (HR = 10.597, p = 0.033).
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Cytotrophoblast
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Proliferation index
Proliferation Marker
Trophoblast
Placentation
Human placental lactogen
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Abstract Background Neuroendocrine tumors (NETs) constitute tumors widely distributed and with heterogeneous biological behavior. For gastrointestinal neuroendocrine tumors (GI-NETs) the following prognostic factors have been identified: location, production of hormones, size and proliferative grade. The latter must be calculated using proliferation index by the number of mitosis or the proportion of tumor cells positive for Ki67 immunostaining. The objective of this study was to use a quantitative tool to calculate the Ki67 index in GI-NETs. Material and methods We reviewed 40 cases of GI-NETs diagnosed at the Department of Pathological Sciences, Santa Casa de Misericórdia de São Paulo Hospital between 2004 and 2014 and compared the Ki67 index by manual count using scanned photomicrographs with semi-automated digital analysis (MC) and eyeball estimation (EE) of the histological slide. After Ki67 immunostaining, the slides were scanned with 3DHistech Pannoramic Scanners. Hot spots were selected and exported in a high-resolution image format and the Ki67 index was calculated with semi-automated image analysis software (AxioVision 3.0). Ki67 immunoreactivity was expressed as the percentage of tumor cells with nuclear staining (number of positive tumor cells/a minimum of 500 total tumor cells). Results We compared the classification of the neuroendocrine tumor by using the two methods in the semi-automated method 26 maintained the same grade, while 14 were re-classified, 4 being upgraded and 10 downgraded. Conclusion In the EE method there was a larger estimate of the percentage of positivity for KI67. As the Ki67 values are the criteria for the classification of neuroendocrine tumors, the semi-automated method can have less error.
Digital Image Analysis
Immunostaining
Ki-67
Proliferation index
Mitotic index
Automated method
Image Analysis
Proliferative index
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Slide digitalization has brought pathology to a new era, including powerful image analysis possibilities. However, while being a powerful prognostic tool, immunostaining automated analysis on digital images is still not implemented worldwide in routine clinical practice.Digitalized biopsy sections from two independent cohorts of patients, immunostained for membrane or nuclear markers, were quantified with two automated methods. The first was based on stained cell counting through tissue segmentation, while the second relied upon stained area proportion within tissue sections. Different steps of image preparation, such as automated tissue detection, folds exclusion and scanning magnification, were also assessed and validated. Quantification of either stained cells or the stained area was found to be correlated highly for all tested markers. Both methods were also correlated with visual scoring performed by a pathologist. For an equivalent reliability, quantification of the stained area is, however, faster and easier to fine-tune and is therefore more compatible with time constraints for prognosis.This work provides an incentive for the implementation of automated immunostaining analysis with a stained area method in routine laboratory practice.
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AIMS: To determine cell proliferation in infiltrating breast carcinomas. METHODS: Using the MIB-1 monoclonal antibody, the proliferation index was measured in paraffin wax sections of 871 breast cancers. The MIB-1 proliferation index was compared with other markers of disease progression: size, lymph node status, histotype, oestrogen and progesterone receptor status, expression of p53 and Neu, and DNA ploidy. All parameters were measured using image analysis. In 347 tumours, the MIB-1 and Ki-67 proliferation indexes were compared. Follow up data were available for 170 cases (median 66.5 months). RESULTS: Of the tumours, 314 (36%) had a high proliferation index. The MIB-1 proliferation index was correlated directly with size, nodal status, overexpression of p53 and Neu, and the DNA index; and inversely with oestrogen and progesterone receptor status. The correlation between MIB-1 and Ki-67 proliferation indexes was statistically significant. In patients with pT1 tumours, a low proliferation index correlated with a longer relapse-free interval and overall survival; node negative patients with a low proliferation index had a longer overall survival. CONCLUSIONS: The MIB-1 proliferation index is a reliable, practical and useful method of measuring proliferative activity and is an important predictor of clinical behaviour.
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Cytotrophoblast
Proliferative index
Proliferation index
Decidua
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Diffuse large B-cell lymphoma (DLBCL) is a high-grade neoplasm that has heterogeneous properties in clinical, morphological, and immunophenotypic aspects. In the present study the effects of p53, Bcl-2, and Ki67 on prognosis and their relationships with clinical parameters were examined. Thirty-five patients who had been diagnosed with nodally located DLBCL at İzmir Atatürk Training and Research Hospital between January 1999 and June 2006 were included in the study. The Ann Arbor classification system was used to determine the stage of the patients. The patients were evaluated according to age, sex, stage, B symptoms, extranodal involvement, and lactate dehydrogenase (LDH) level as well as immunohistochemically. P53 protein and Bcl-2 oncoprotein expressions and Ki67 proliferation index were assessed immunohistochemically.High Bcl-2 expression was found in 9 patients (25.7%), high p53 expression was found in 10 patients (28.6%), and high Ki67 was observed in 23 patients (65.7%). There was no significant correlation between p53 expression, Bcl-2 expression, or Ki67 proliferation index and age, sex, stage, B symptoms, extranodal involvement, LDH level, and overall survival (p>0.05). We did not find a relationship among p53 expression, Bcl-2 expression, Ki67 proliferation index, and prognosis (p>0.05). There was no significant relationship between overall survival and age, sex, stage, B symptoms, extranodal involvement, or LDH level (p>0.05). Our results revealed that Bcl-2 and p53 protein expressions and Ki67 proliferation index have no effect on overall survival of patients with DLBCL.The prognostic importance of p53 and Bcl-2 protein expressions and Ki67 proliferation index in DLBCL, which has biological and clinical heterogeneity, can be understood in a large series of studies that have subclasses and immunohistochemical markers with optimal cut-off values.None declared.Amaç: Diffuz büyük hücreli B lenfoma (DBBHL) klinik, morfolojik, immunofenotipik ve genetik özellikleri ile heterojenite gösteren yüksek dereceli bir neoplazmdır. Çalışmamızda DBBHL’da hücre siklusu düzenleyicisi olan p53 (tümör supresör gen), apoptozisi inhibe edici onkoprotein olan Bcl-2 ve hücre proliferasyon belirleyicisi olan Ki67 ekspresyonlarının klinik parametrelerle ilişkisini ve prognoz üzerindeki etkilerini araştırdık. Gereç ve Yöntemler: Çalışmaya Ocak 1999 - Haziran 2006 tarihleri arasında, İzmir Atatürk Eğitim ve Araştırma Hastanesi Patoloji Bölümü’nde, nodal yerleşimli DBBHL tanısı alan 35 olgu alındı. Hastaların evrelemesinde Ann Arbor sınıflaması kullanıldı. Olgular yaş, cinsiyet, evre, B semptomları, ekstranodal tutulum, LDH düzeyi ve sağ kalımları yanı sıra immünohistokimyasal olarak; p53 protein ekspresyonu, Bcl-2 onkoprotein ekspresyonu ve Ki67 proliferasyon indeksi açısından değerlendirildi.Bulgular: Yüksek Bcl-2 ekspresyonu 9 hastada (%25.7), yüksek p53 ekspresyonu 10 hastada (%28.6), yüksek Ki67 ekspresyonu 23 hastada (%65.7) saptandı. p53 ekspresyonu, Bcl-2 protein ekspresyonu ve Ki67 proliferasyon indeksi ile yaş, cinsiyet, evre, B semptomları, ekstranodal tutulum, LDH düzeyi ve sağ kalım arasında istatistiksel olarak anlamlı ilişki saptanmadı (p>0.05). Tüm olgularda p53, Bcl-2 ve Ki67 ekspresyonları ile prognoz arasındaki ilişki istatistiksel olarak anlamlı değildi (p>0.05). Sağ kalım süresi ile yaş, cinsiyet, evre, B semptomları, ekstranodal tutulum ve LDH düzeyi arasında istatistiksel olarak anlamlı ilişki bulunmadı (p>0.05). Bu çalışmada Bcl-2 ve p53 protein ekspresyonları ile Ki67 proliferasyon indeksinin, DBBHL’lı hastaların yaşam süresi üzerinde etkili olmadığı bulunmuştur. Sonuç: p53 ve Bcl-2 protein ekspresyonları ile Ki67 proliferasyon indeksinin, DBBHL’daki prognostik önemi, immunohistokimyasal markerların optimal cut-off değerlerinin belirlendiği, subgrup ayrımının yapıldığı daha geniş serili çalışmalarda, biyolojik ve klinik heterojenitesi olan bu hastalıkta, daha net anlaşılacaktır. Anahtar Sözcükler: Diffüz büyük B hücreli lenfoma, p53, Bcl-2, Ki67, Prognoz.
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Summary The DSS (dextran sulfate sodium) model of colitis is a mouse model of inflammatory bowel disease. Microscopic symptoms include loss of crypt cells from the gut lining and infiltration of inflammatory cells into the colon. An experienced pathologist requires several hours per study to score histological changes in selected regions of the mouse gut. In order to increase the efficiency of scoring, Definiens Developer software was used to devise an entirely automated method to quantify histological changes in the whole H&E slide. When the algorithm was applied to slides from historical drug-discovery studies, automated scores classified 88% of drug candidates in the same way as pathologists' scores. In addition, another automated image analysis method was developed to quantify colon-infiltrating macrophages, neutrophils, B cells and T cells in immunohistochemical stains of serial sections of the H&E slides. The timing of neutrophil and macrophage infiltration had the highest correlation to pathological changes, whereas T and B cell infiltration occurred later. Thus, automated image analysis enables quantitative comparisons between tissue morphology changes and cell-infiltration dynamics.
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