Tissue architectural features for the grading of prostatic carcinoma

In research for the development of a computer-aided workstation for the objective grading of prostatic carcinoma, tissue architectural (histometric) features were analyzed in ten cases each of well-differentiated, moderately differentiated and poorly differentiated carcinoma (as subjectively graded by the consensus of a panel of experts). Sections were cut at 4 microns, stained by the Feulgen reaction and digitized by two different video-based photometric systems. Some images were interactively segmented, considering the histometric clues to be studied; others were automatically segmented by an expert system-guided technique. The latter procedure produced good results, with over 90% of the nuclei judged to be correctly segmented in 64% of the fields studied and over 80% in another 24% of the fields. While the number of nuclei per field provided some separation of well-differentiated from other lesions, the number of nuclei per gland distinguished between well-differentiated and moderately differentiated lesions. Simplicial decomposition of the images also provided a measure of the degree of differentiation, as did the "texture" of the nuclear placement, based on two run-length statistics. Combination of the run-length features distinguished the three categories of lesions with statistical significance. The results of this study provided insights into the problems (such as the effect of field boundaries) faced in the design of an computer-aided grading system. They also showed the value of expert system-guided scene segmentation and of such histometric features as the field cellularity and the number of nuclei per gland for the discrimination between lesions of different grades of differentiation.     

Value of the cribriformity factor in grading prostatic carcinoma.

Architectural and histometric features for the objective grading of prostate adenocarcinoma in histologic specimens were analyzed in five cases each of Gleason primary grades 2, 3A, 3B, 3C, 4A and 4B, selected as "typical" for the histopathologic images. Tissue sections from the selected cases were stained by the Feulgen method. Fifteen fields for each grade, for a total of 4,430 glands, were digitized by a video-based microphotometer at low resolution (pixel spacing of 2 microns). Outer and inner outlines of the glandular epithelium were traced manually using a mouse. For each field the number of glands, the gland area, the lumen area, the area of the glandular epithelium and the cribriformity factor were computed. The gland area and its variance proved to be useful indicators for lower-grade lesions, whereas the variance of cribriformity resulted in an excellent grading indicator in the Gleason 3-4 range when cribriform glands were present.     

Correlation of modified Gleason grading with pT stage of prostatic carcinoma after radical prostatectomy

OBJECTIVE: To study the correlation between modified Gleason score (GS) and pT stage of radical prostatectomy (RP) specimens. STUDY DESIGN: Six hundred forty-nine consecutive RP specimens were graded according to the conventional and the modified Gleason grading systems. RESULTS: A total of 29% of the tumors were upgraded. Both variants of GS correlated with pathologic stage. Stage pT2 tumors were assigned a GS of 3-6 less often with modified grading than with conventional grading (29% and 84%, respectively). The only significant difference of stage distribution between conventional and modified GS was for GS 7, where pT2 was the most common stage with modified grading (54%) and pT3 was most common with conventional grading (67%). Of GS 3 + 4 = 7a tumors, 95% were stage pT2, while 79% of GS 4 + 3 = 7b tumors were stage pT3-4. CONCLUSION: The stage distribution of modified GSs of RP specimens differs from that of conventional GSs, but a good correlation exists between grade and pT stage. Notably, GS 4 + 3 = 7b was more often associated with high stage than was GS 3 + 4 = 7a.     

Computer-assisted grading of adenocarcinoma in prostatic aspirates

Conventional cytologic grading of fine needle aspirates of prostatic adenocarcinoma has been shown neither to be reproducible nor to correlate well with histologic grading. This study developed a tumor grade classification based on computerized cytomorphometric features and compared the results to conventional grading of companion tissue sections. The image analysis system evaluated architectural features of the aspirates (mainly cell cluster features and interrelationships) as well as nuclear features. Thirty-five prostatic adenocarcinomas (8 well, 19 moderately and 8 poorly differentiated) were evaluated. Discriminant functions based on data collected at medium and high resolution distinguished between aspirates from low-grade (well-differentiated) and high-grade (poorly differentiated) adenocarcinomas with 81% accuracy. Moderately differentiated cancers could not be classified as a distinct group. This study suggests that accurate grading of prostatic adenocarcinoma in fine needle aspirate smears requires the evaluation of medium-resolution features related to specimen cellularity and uniformity or crowding of cell clusters as well as of high-resolution features of nuclear area, perimeter and coarseness of chromatin texture. These findings are compared to those of other schemes for the cytologic grading of prostatic aspirates.     

Architectural, morphometric and photometric features and their relationship to the main subjective diagnostic clues in the grading of prostatic cancer

Novel software was developed to perform quantitative measurements of architectural and nuclear features in tissue sections. A pilot study was then undertaken to determine the diagnostic relevance of these quantitative features in prostatic tissue and the relationship of these objective features to the subjective clues used by practicing pathologists in the grading of prostatic adenocarcinoma. From a group of 82 cases of adenocarcinoma of the prostate with long-term follow-up, a subset of 15 cases that included 5 each in Mostofi grades I, II and III was carefully selected for analysis. Consecutive sections from each case were stained with hematoxylin and eosin or the Feulgen stain for visual and cytometric evaluations, respectively. The most important differences in the objective architectural features observed between the Mostofi grade I and II cases were the number of nuclei per gland and their distance from the glandular center. Significant differences were also noted in gland size and the variation in gland size. The Mostofi grades were also significantly different in terms of quantitative high-resolution features measuring nuclear size and its variation, total nuclear DNA content and the proportion of very aneuploid nuclei. There was a fairly good agreement between many of the subjective diagnostic clues and their corresponding quantitative architectural and nuclear features. This work (1) significantly extended the capabilities of our PC-based microphotometer system to analyze glandular tissue specimens, (2) provided insight into the objective bases for the expert diagnosis of adenocarcinomas of the prostate and (3) gave preliminary evidence of the ability of quantitative architectural features and high-resolution cytometric features to discriminate between the major diagnostic categories of these lesions.     

The evolution of hormonal therapy for prostatic carcinoma

It is well recognized that testosterone has a number of untoward effects on prostatic carcinoma and that castration is associated with significant tumor shrinkage and resolution of symptoms of advanced prostatic carcinoma. Approaches to hormonal therapy have evolved significantly over the last several decades. Initially castration was utilized, which provided effective reduction of testicular androgens, but with adverse psychological factors. The next approach was utilization of diethylstilbestrol, but with significant cardiovascular toxicity in higher doses. The development of the luteinizing hormone-releasing hormone agonists provided an improvement in pharmacologic castration; however, they are associated with a transient testosterone surge and the potential for exacerbation of clinical manifestations of advanced prostate carcinoma (the so-called "testosterone flare"). Recently, gonadotropin-releasing hormone (GnRH) antagonists have been investigated. Abarelix is a pure GnRH antagonist that blocks the anterior pituitary receptor, resulting in prompt and significant reduction not only of luteinizing hormone but also follicle-stimulating hormone. This results in castrate levels of testosterone while avoiding the testosterone surge.     

Tumour ploidy, morphometry, histological grading and clinical features in ovarian carcinoma: mutual relations

The relationship between tumour ploidy and qualitative and quantitative histopathology was assessed in a series of 95 ovarian carcinomas. 67% of the tumours were non-diploid (DNA aneuploid). 56% of the early stage (I-II) tumours were non-diploid and 81% of the tumours in advanced (III-IV) stages were aneuploid. Histological grading failed to show a clear relationship between increasing malignancy grade and ploidy. There was a close association between DNA ploidy and nuclear perimeter, area and shortest and longest nuclear diameter: the nuclei of non-diploid tumours were generally larger. Also the number of mitotic figures per square millimeter of epithelium in the microscope image (volume-corrected mitotic index, M/V-index) differed significantly between near-diploid and non-diploid tumours. Discriminant analysis showed that 74% of the learning-set tumours (67% of the test set tumours) could be correctly classified in low-ploidy and high-ploidy categories with morphometric features (nuclear perimeter, M/V-index and volume percentage of epithelium). Characteristic features of non-diploid ovarian tumours--rapid proliferation and large nuclear size--could be assessed with morphometric methods which allowed a relatively large aneuploid tumour group to be distinguished.     

Causal probabilistic modeling for malignancy grading in pathology with explanations of dependency to the related histological features

This work demonstrates that histological grading of brain tumors and astrocytomas can be accurately predicted and causally explained with the help of causal probabilistic models, also known as Bayesian networks (BN). Although created statistically, this allows individual identification of the grade of malignancy as an internal cause that has enabled the development of the histological features to their observed state. The BN models are built from data representing 794 cases of astrocytomas with their malignant grading and corresponding histological features. The computerized learning process is improved when pre-specified knowledge (from the pathologist) about simple dependency relations to the histological features is taken into account. We use the BN models for both grading and causal analysis. In addition, the BN models provide a causal explanation of dependency between the histological features and the grading. This can offer the biggest potential for choice of an efficient treatment, since it concentrates on the malignancy grade as the cause of pathological observations. The causal analysis shows that all ten histological features are important for the grading. The histological features are causally ordered, implying that features of first order are of higher priority, e.g. for the choice of treatment in order not to allow the malignancy to progress to a higher degree. Due to the explanations of feature relations, the causal analysis can be considered as a powerful complement to any malignancy classification tool and allows reproducible comparison of malignancy grading.     

MRI-based radiomics signature for tumor grading of rectal carcinoma using random forest model

The present study aimed to construct prospective models for tumor grading of rectal carcinoma by using magnetic resonance (MR)-based radiomics features. A set of 118 patients with rectal carcinoma was analyzed. After imbalance-adjustments of the data using Synthetic Minority Oversampling Technique (SMOTE), the final data set was randomized into the training set and validation set at the ratio of 3:1. The radiomics features were captured from manually segmented lesion of magnetic resonance imaging (MRI). The most related radiomics features were selected using the random forest model by calculating the Gini importance of initial extracted characteristics. A random forest classifier model was constructed using the top important features. The classifier model performance was evaluated via receive operator characteristic curve and area under the curve (AUC). A total of 1,131 radiomics features were extracted from segmented lesion. The top 50 most important features were selected to construct a random forest classifier model. The AUC values of grade 1, 2, 3, and 4 for training set were 0.918, 0.822, 0.775, and 1.000, respectively, and the corresponding AUC values for testing set were 0.717, 0.683, 0.690, and 0.827 separately. The developed feature selection method and machine learning-based prediction models using radiomics features of MRI show a relatively acceptable performance in tumor grading of rectal carcinoma and could distinguish the tumor subjects from the healthy ones, which is important for the prognosis of cancer patients.     

Serum prostate-specific antigen determination in prostatic carcinoma

Prostate-specific antigen (PSA) is a tissue-specific glycoprotein identified by Wang in 1979. It is synthesized in the prostate independently of prostatic acid phosphatase (PAP). A total of 199 subjects were divided into four groups: controls aged less than 50 years, controls aged more than 50 years, patients with benign prostatic hyperplasia (BPH) and patients with prostatic carcinoma. PSA cut-off value was set at 10 ng/ml (mean for the BPH group plus 2 SD). With this cut-off value PSA could not be used as an early predictor of prostatic carcinoma. The association of PSA and PAP in prostatic cancer increases the number of patients with positive biological markers.     

Steroid hormone regulation of prostatic acid phosphatase expression in cultured human prostatic carcinoma cells

We have investigated the modulation of prostatic acid phosphatase expression in the human prostatic cancer cell line LNCaP in response to the natural androgens testosterone and dihydrotestosterone, the female sex steroid estradiol and the synthetic androgen R1881 (methyltrienolone). Testosterone and dihydrotestosterone at 1 microgram/ml enhance the acid phosphatase synthesis by a factor of 3.5, while a hundred-fold lower concentration of the synthetic androgen R1881 induces an almost five-fold increase in the expression of this enzyme. The stimulation by all androgens tested and estradiol was dose-dependent. The synthetic glucocorticoid triamcinolone acetonide does not modulate the prostatic acid phosphatase expression in LNCaP cells, neither alone nor in combination with R1881.