Computer approach to recognition of Fuhrman grade of cells in clear-cell renal cell carcinoma.

OBJECTIVE: To present a computerized system for recognition of Fuhrman grade of cells in clear-cell renal cell carcinoma on the basis of microscopic images of the neoplasm cells in application of hematoxylin and eosin staining. STUDY DESIGN: The applied methods use combined gradient and mathematical morphology to obtain nuclei and classifiers in the form of support vector machine to estimate their Fuhrman grade. The starting point is a microscopic kidney image, which is subject to the advanced methods of preprocessing, leading finally to estimation of Fuhrman grade of cells and the whole analyzed image. RESULTS: The results of the numerical experiments have shown that the proposed nuclei descriptors based on different principles of generation are well connected with the Fuhrman grade. These descriptors have been used as the diagnostic features forming the inputs to the classifier, which performs the final recognition of the cells. The average discrepancy rate between the score of our system and the human expert results, estimated on the basis of over 3,000 nuclei, is below 10%. CONCLUSION: The obtained results have shown that the system is able to recognize 4 Fuhrman grades of the cells with high statistical accuracy and agreement with different expert scores. This result gives a good perspective to apply the system for supporting and accelerating the research of kidney cancer.

Nucleolus detection in the Fuhrman grading system for application in CCRC.

The paper presents a method for nucleolus detection in images of nuclei in clear-cell renal carcinoma (CCRC). The method is based on the similarity of the nuclei image and the two-dimensional paraboloidal window function. The results of numerical experiments performed on almost 2600 images of CCRC nuclei have confirmed the good accuracy of the method. The developed algorithm will be used to accelerate further research in computer-assisted diagnosis of CCRC.

Computer system for cell counting in selected brain tumors at Ki-67 immunohistochemical staining.

OBJECTIVE: To present a computerized system for cell counting in histopathologic slides of meningioma and oligodendroglioma stained immunohistochemically against Ki-67 antigen and discuss the variability of tumor cell numbers in the field of view of analyzed neoplasms to standardize tumor cellularity. STUDY DESIGN: A computer program using an algorithm based on mathematical morphology was developed to perform quantitative evaluation of slides. That solution was combined with the Support Vector Machine used for classification of cell immunoreactivity. RESULTS: The mean number of cells in the analyzed field of view from patients with meningioma was 623. Of these, 95% were in the 386-781 cells range. In oligodendrogliomas the mean was 474 cells and all results were in the 204-736 range. The mean relative discrepancy between results of our system and human expert score was 8%. CONCLUSION: The proposed system appeared to be an efficient tool for supporting histopathologic diagnosis. The applied sequential thresholding simulated well the human process of cell recognition. Cellularity of the analyzed tumors did not show stability within the specimens from different patients. The results were also highly variable in different fields of view obtained from the same patient.

Comparative analysis of methods for accurate recognition of cells through nuclei staining of Ki-67 in neuroblastoma and estrogen/progesterone status staining in breast cancer.

OBJECTIVE: To compare 2 automatic systems for the recognition and counting of 2 different families of cells through nuclei staining: Ki-67 in neuroblastoma and estrogen/progesterone (ER/PR) status staining in breast cancer. STUDY DESIGN: Morphology-based segmentation strategies and the Support Vector Machine approach have been used for the accurate extraction and recognition of the cells. To achieve the highest possible accuracy, 2 specialized systems specially suited for Ki-67 and ER/PR staining have been developed. RESULTS: The testing set of histologic slides of Ki-67 and ER/PR staining has been assessed by our system and the results compared to the score of a human expert. The results are in good agreement. The average differences are within the acceptable limits of 10%. The main advantage of the system is its absolute repeatability of scores. CONCLUSION: The proposed computer-assisted automatic system of cell extraction and recognition through nuclei staining has confirmed sufficient accuracy for the tested images and may provide a useful tool for cell recognition and counting on the basis of histologic slides with Ki-67 and ER/PR staining.

New automated image analysis method for the assessment of Ki-67 labeling index in meningiomas.

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.