Quantitative Nuclear Morphometry Research Articles

Advances in the computational and molecular understanding of the prostate cancer cell nucleus.

Nuclear alterations are a hallmark of many types of cancers, including prostate cancer (PCa). Recent evidence shows that subvisual changes, ones that may not be visually perceptible to a pathologist, to the nucleus and its ultrastructural components can precede visual histopathological recognition of cancer. Alterations to nuclear features, such as nuclear size and shape, texture, and spatial architecture, reflect the complex molecular-level changes that occur during oncogenesis. Quantitative nuclear morphometry, a field that uses computational approaches to identify and quantify malignancy-induced nuclear changes, can enable a detailed and objective analysis of the PCa cell nucleus. Recent advances in machine learning-based approaches can now automatically mine data related to these changes to aid in the diagnosis, decision making, and prediction of PCa prognoses. In this review, we use PCa as a case study to connect the molecular-level mechanisms that underlie these nuclear changes to the machine learning computational approaches, bridging the gap between the clinical and computational understanding of PCa. First, we will discuss recent developments to our understanding of the molecular events that drive nuclear alterations in the context of PCa: the role of the nuclear matrix and lamina in size and shape changes, the role of 3-dimensional chromatin organization and epigenetic modifications in textural changes, and the role of the tumor microenvironment in altering nuclear spatial topology. We will then discuss the advances in the applications of machine learning algorithms to automatically segment nuclei in prostate histopathological images, extract nuclear features to aid in diagnostic decision making, and predict potential outcomes, such as biochemical recurrence and survival. Finally, we will discuss the challenges and opportunities associated with translation of the quantitative nuclear morphometry methodology into the clinical space. Ultimately, accurate identification and quantification of nuclear alterations can contribute to the field of nucleomics and has applications for computationally driven precision oncologic patient care.

Abstract 4352: Prediction of prostate cancer progression with biomarkers and tissue morphometry changes

Abstract Introduction: The progression of prostate cancer (PCa) involves both tissue morphometric changes and critical molecular alterations (such as cancer-associated markers). We have studied 80 men that underwent radical prostatectomy (RP) to evaluate this dual approach to discriminate Gleason score and progression. Next, we applied our approach at the diagnostic biopsy of men that chose active surveillance (AS) as an option for the management of their PCa, to predict the likelihood for re-classification of their disease to immediate treatment. Materials & Methods: Two tissue microarrays (TMAs) with 80 RP PCa cases, stratified by Gleason scores were used first. Next, a total of 27 favorable and 24 unfavorable AS biopsy PCa cases were evaluated. In both series we applied multiplex tissue immunoblotting (MTI) & quantitative nuclear morphometry studies. Data were first analyzed alone and then in combination using multivariate logistic regression (MLR) to predict the aggressive RP cases or AS biopsy reclassification status of the cancer. MTI was used to simultaneously detect 5∼6 biomarkers ((-5,-7)ProPSA, PCNA, RBM3, Her2/neu, & CACNA1D, etc) on a single 5 micron section. Proteins on the slide were transferred onto a series of 5∼6 P-film membranes and each membrane was probed with different primary antibody. The quantified FITC fluorescence signal of the biomarker were normalized to CY5 labelled total protein. For the nuclear morphometry analysis, quantification of the nuclear features were achieved either using ImagePro Premier 9.1 Software for the TMAs or the adaptive active contour scheme (AdACM) that uses nuclear shape, architectural and textural features extracted from AS biopsies. RESULTS: Using MLR, to differentiate aggressive PCa (Gleason score 4+3 & > = 8) from less aggressive PCa (Gleason score 3+3 & 3+4) on the TMAs of RP cases, our biomarkers model generated an ROC-AUC of 0.8 with accuracy of 71.43%, while morphometry model generate an ROC-AUC of 0.92 with accuracy of 82.50 %. When combined, it improved to an ROC-AUC of 0.96 and accuracy of 87.01%. Additionally, MLR was used for differentiation unfavorable biopsy cases that requires reclassification due to upgraded Gleason score, increased tumor volume, and/or PSA/PSAD during monitoring and need definitive treatment. On the contrary, favorable cases are very low risk (VLR) PCa biopsies that are not reclassified and can continue monitoring. The biomarkers model produced an ROC-AUC of 0.71 with an accuracy of 73.91% while morphometry model produces an ROC-AUC of 0.84 and accuracy of 74.51%. When combined, the new model produces an ROC-AUC of 0.88 and accuracy of 80.43%. CONCLUSIONS: Our method combining tissue morphometry with biomarkers demonstrated its translational clinical relevance since it can predict PCa aggressiveness in men that have undergone RP. Also, this approach predicts AS PCa cases requiring reclassification and immediate treatment at biopsy. Citation Format: Guangjing Zhu, George Lee, Christine Davis, Luciane Tsukamoto Kagohara, Jonathan I. Epstein, Patricia Landis, H. Ballantine Carter, Anant Madabhushi, Robert W. Veltri. Prediction of prostate cancer progression with biomarkers and tissue morphometry changes. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4352. doi:10.1158/1538-7445.AM2015-4352

Abstract 1139: Extracellular matrix components direct chromatin texture and nuclear morphological changes in epithelial-mesenchymal transition

Abstract Background: Epithelial-mesenchymal transition (EMT) is associated with metastasis in human prostate cancer (PCa). As metastatic tumors are the major cause of death in most cancer patients, identifying instances of EMT in patients may serve as a good prognostic indicator of progressive disease. Abnormal morphologic deformation of the cell nucleus is strongly associated with an aggressive cancer phenotype and computer-aided quantitative nuclear morphometry (QNM) has been used to quantify this deformation. In the present study, we go further to investigate the association of nuclear morphologic features between an epithelial PCa phenotype and an EMT-induced mesenchymal PCa phenotype across a panel of extracellular matrix (ECM) substrates on which cells are grown in vitro. Methods: PC3 epithelial cells (PC3-Epi) are established human PCa cultures from bone metastatic lesions. M2 macrophages induced PC3-Epi cells to undergo EMT and become mesenchymal PC3 cells (PC3- EMT). PC3-Epi and PC3-EMT cells were cultured independently on glass tissue-culture slides and also on a panel of ECM component substrates, including collagen 1, poly-D-Lysine, and various molecular weight hyaluronic acid polymers. Slides were stained with Feulgen or hematoxylin and eosin (H&E). The images were digitized with a slide-scanning microscope. Computer aided image processing software measured a total of 41 non-redundant features. This included nuclear size, shape, and texture features. Features were statistically analyzed using both supervised and unsupervised machine learning algorithms. Results: Statistical classification between PC3-Epi and PC3-EMT was performed on a panel of ECM substrates. The PC3-Epi and PC3-EMT morphological features were characterized by the type of ECM substrate on which cells were cultured. Chromatin texture and shape features, such as Feret diameter, were among the most significant in contributing to separation of the PC3-Epi and PC3-EMT. In contrast, the morphological features could not be separated when cells were grown on glass surfaces. Conclusions: The PC3-Epi and PC3-EMT cells were statistically separated more effectively with extracellular matrix substrates than with glass. Furthermore, our findings suggest that ECM components affect chromatin texture, a function of chromatin organization, and nuclear morphology. Our results also indicate quantitative nuclear morphometric analysis may be a viable tool for demonstrating instances of EMT in clinical samples, potentially adding quantitative prognostic value to this technique. Nuclear morphometric factors, such as those used here, have also found applications to distinguish aggressive human prostate cancers in fixed biopsy samples (See review: Veltri, R.W., et al. “Nuclear morphometry, nucleomics and prostate cancer progression”. Asian Journal of Andrology (2012) 14, 375-384.) Citation Format: James E. Verdone, Robert W. Veltri, Steven M. Mooney, James R. Hernandez, Calvin A. Harberg, Donald S. Coffey, Kenneth J. Pienta. Extracellular matrix components direct chromatin texture and nuclear morphological changes in epithelial-mesenchymal transition. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 1139. doi:10.1158/1538-7445.AM2014-1139

Value of computer‐assisted quantitative nuclear morphometry for differentiation of reactive renal tubular cells from low‐grade urothelial carcinoma

To assess whether computer-assisted quantitative morphological parameters can be an effective tool for objectively distinguishing reactive renal tubular cells from low-grade urothelial carcinoma cells (LG-UCs) in voided urine. Nuclear morphometry was performed by a computer-assisted image analyser system on Papanicolaou-stained cytological specimens. The circumference of reactive renal tubular cells (n = 40) or LG-UC (n = 20) nuclei were manually traced, and the following nuclear morphometric parameters were analysed: (i) area, (ii) perimeter, (iii) roundness factor, (iv) maximum length, and (v) linear factor. For each nuclear measurement, we calculated the maximum, minimum, mean and standard deviation. The mean nuclear area and nuclear perimeter were higher in reactive renal tubular cells compared to the LG-UCs. The mean of roundness and linear factors (reflecting a tendency for the nuclear outline to be regular and oval, respectively) were higher in LG-UCs compared with reactive renal tubular cells. Among nuclear areas, the nuclear perimeter, roundness factors and maximum length did not show any significant differences between reactive renal tubular cells and LG-UCs. On the other hand, the linear factor showed a mean higher value among LG-UCs than reactive renal tubular cells (P = 0.023). Of five quantitative nuclear morphological parameters, only linear factor was statistically significant in differentiating reactive renal tubular cells in renal disease from LG-UCs.

Comparative morphometric analysis of recurrent and nonrecurrent canine basal cell carcinomas: a preliminary report

Most reports of canine basal cell carcinomas (BCCs) focus on morphologic appearance rather than biologic behavior. Rare recurrences and no metastases have been reported in dogs. Quantitative nuclear morphometry may be useful in predicting tumor recurrence. The aim of the present study was to compare quantitative nuclear parameters of canine BCCs that did not recur within 60 weeks of excision with those that recurred. Cytologic specimens obtained from spontaneous BCCs from 11 dogs were analyzed by computerized nuclear morphometry. The dogs were monitored by their owners over a period of 60 weeks to detect local recurrence of the tumor; recurrent tumors were confirmed histologically. For each initial tumor specimen, nuclei of at least 100 neoplastic cells were measured by 2 independent observers, and mean nuclear area (MNA), mean nuclear perimeter (MNP), and mean nuclear diameter (MND) were calculated. Six nonrecurrent and 5 recurrent tumors were analyzed. Neoplastic cells from BCCs that subsequently recurred had higher MNA (102.41 +/- 4.57 microm(2)), MNP (36.27 +/- 0.61 microm), and MND (11.21 +/- 0.27 microm) than cells from nonrecurrent BCCs (MNA 87.66 +/- 4.79 microm(2), MNP 33.51 +/- 0.78 microm, MND 10.36 +/- 0.29 microm) (P<.001; Mann-Whitney U-test). Based on these preliminary results, nuclear morphometry may be a useful tool to predict local recurrence of BCCs in dogs.

Abstract A10: Gene expression analysis of malignancy associated changes in normal bronchial epithelium

Abstract A10 Background Lung cancer is the number one cancer-related cause of death in the world. The poor prognosis for lung cancer patients is largely due to the lack of an effective non-invasive method to detect the disease in its early stage. Using quantitative nuclear morphometry of sputum cells, we have previously demonstrated that in the presence of tumour, cytologically normal appearing diploid cells may exhibit malignancy-associated changes (MACs) and that these changes may be used as surrogate markers for early detection. The molecular basis for MACs is not known. Objective Our objective is to identify malignancy-associated gene expression changes in normal bronchial epithelial cells obtained by bronchial brushing. Methods We compared the pre- and post-surgical resection gene expression profiles of bronchial epithelial cells obtained by brushing of airways in the lung opposite to the one containing the lung cancer. Agilent whole genome expression arrays were used to generate mRNA transcript profiles for seven paired normal bronchial brushings taken before and 3 to 6 months after surgical resection of the tumour. Ingenuity Pathway Analysis software was used to identify downstream expression targets of the soluble signaling factors VEGF, TGF-β, TNF-α, and IL-8, which have been reported to be secreted by lung tumour cells. The target genes identified were analyzed for malignancy induced differential gene expression between normal bronchial epithelial cells from cancerous and non-cancerous environments. Results We detected differential expression of multiple target genes in the bronchial brush cells. Genes that were found to be differentially expressed included downstream targets of the signaling molecules EGF,TGF-β, and VEGF. Conclusion Soluble signaling molecules secreted by lung malignancies induce MACs, as evident by altered gene expression after removal of the tumour. Although further investigation is necessary, our results are consistent with previous observation that lung tumours can induce MACs in normal appearing bronchial cells in the vicinity of the tumor. MACs may be a potential marker for early detection of lung cancer. This data also provides evidence that MACs can be measured quantitatively in bronchial brush cells using gene expression analysis. Citation Information: Cancer Prev Res 2008;1(7 Suppl):A10.

Prediction of Prostate-Specific Antigen Recurrence in Men with Long-term Follow-up Postprostatectomy Using Quantitative Nuclear Morphometry

Nuclear morphometric signatures can be calculated using nuclear size, shape, DNA content, and chromatin texture descriptors [nuclear morphometric descriptor (NMD)]. We evaluated the use of a patient-specific quantitative nuclear grade (QNG) alone and in combination with routine pathologic features to predict biochemical [prostate-specific antigen (PSA)] recurrence-free survival in patients with prostate cancer. The National Cancer Institute Cooperative Prostate Cancer Tissue Resource (NCI-CPCTR) tissue microarray was prepared from radical prostatectomy cases treated in 1991 to 1992. We assessed 112 cases (72 nonrecurrences and 40 PSA recurrences) with long-term follow-up. Images of Feulgen DNA-stained nuclei were captured and the NMDs were calculated using the AutoCyte system. Multivariate logistic regression was used to calculate QNG and pathology-based solutions for prediction of PSA recurrence. Kaplan-Meier survival curves and predictive probability graphs were generated. A QNG signature using the variance of 14 NMDs yielded an area under the receiver operator characteristic curve (AUC-ROC) of 80% with a sensitivity, specificity, and accuracy of 75% at a predictive probability threshold of > or =0.39. A pathology model using the pathologic stage and Gleason score yielded an AUC-ROC of 67% with a sensitivity, specificity, and accuracy of 70%, 50%, and 57%, respectively, at a predictive probability threshold of > or =0.35. Combining QNG, pathologic stage, and Gleason score yielded a model with an AUC-ROC of 81% with a sensitivity, specificity, and accuracy of 75%, 78%, and 77%, respectively, at a predictive probability threshold of > or =0.34. PSA recurrence is more accurately predicted using the QNG signature compared with routine pathology information alone. Inclusion of a morphometry signature, routine pathology, and new biomarkers should improve the prognostic value of information collected at surgery.

Validation of World Health Organization/International Society of Urologic Pathology 2004 Classification Schema for Bladder Urothelial Carcinomas Using Quantitative Nuclear Morphometry: Identification of Predictive Features Using Bootstrap Method

Despite the introduction of the new World Health Organization 2004 grading classification, the grading of urothelial carcinoma remains difficult and subjective. The aim of this study was to evaluate the role of computer-assisted image morphometric analysis as a tool to improve the objectivity of histologic grading of urothelial carcinoma. A total of 75 urinary bladder biopsies from a cohort of patients with a first-time diagnosis of urothelial carcinoma representing low-grade (n = 19) and high-grade (n = 56) urothelial carcinoma were evaluated. Quantitative nuclear morphometry was performed on these biopsies using approximately 80 to 100 cells per case. A total of 17 nuclear morphometry features were extracted, and a bootstrap-based predictor selection using stepwise logistic regression analysis was performed. Subsequently, a validation was performed using the five top features from the logistic regression analyses by implementing a nonparametric discriminant analysis to identify the most discriminative features that predicted for high-grade cases. The bootstrap technique included nuclear pleomorphism as the most frequently selected predictor of high-grade urothelial carcinoma (in 213 of 500 replicates). Validation using the top five features in the logistic regression analysis method (pleomorphism, configuration run length, DNA mass, feret-Y, and age) using discriminant analysis gave a resubstitution error of 4%, indicating the usefulness of the selected predictors. The present study is the first to provide a morphometric validation of the World Health Organization 2004 system for pathologic grading of bladder cancer. Furthermore, quantitative nuclear morphometry could aid in the objective grading of urinary bladder biopsies. This information might aid the treating physicians in better risk stratification of patients with urothelial carcinoma.

Correlating Routine Cytology, Quantitative Nuclear Morphometry by Digital Image Analysis, and Genetic Alterations by Fluorescence In Situ Hybridization to Assess the Sensitivity of Cytology for Detecting Pancreatobiliary Tract Malignancy

Routine cytologic (RC), fluorescence in situ hybridization (FISH), digital image analysis (DIA), and quantifiable morphometric results from 284 pancreatobiliary stricture brushings were compared. We chose specific DIA nuclear features assessed by pathologists in evaluating RC specimens, such as area and shape. A visual nuclear morphometric score (VNMS) was calculated. There was a difference (P < .001) in the mean VNMS when RC results were classified as negative (11.5), atypical (12.5), suspicious (13.8), and positive (16.5). The mean VNMS of specimens diagnosed as disomy (11.3), trisomy 7 (12.1), and polysomy (14.7) by FISH was also different (P < .001). There was no difference in the VNMS of false-negative and true-negative cytologic specimens (P = .225). Our findings substantiate the relationship between cell nuclear visual alterations and genetic FISH abnormalities. The low sensitivity of cytologic examination for pancreatobiliary carcinoma is due to an absence of tumor cells or the presence of well-differentiated tumor lacking recognizable nuclear atypia.

Significant variations in nuclear structure occur between and within Gleason grading patterns 3, 4, and 5 determined by digital image analysis

Alterations in nuclei structure and DNA content captured from Gleason grading patterns 3, 4 and 5 of radical prostatectomy (RP) cases were determined by a computer-assisted microscope. Quantitative Nuclear Morphometry (QNM) profiles were created to evaluate variability in nuclear structure within each of these grades. A tissue microarray (TMA) was constructed using RP cases and the prostate cancer (PCa) TMA cores prepared from 20 GG-3, 9 GG-4, 10 GG-5 patterns, and 20 benign cancer-adjacent cases from RP archival paraffin blocks. Feulgen-stained nuclei were captured from 0.6 mm spots using the AutoCyte system. Pools of 1100 nuclei captured from each test group were used to calculate Multivariate Logistic Regression (MLR) models that generated predictive indices and predictive probabilities (PP) to make comparisons between and within each set of pooled nuclei. A single QNM profiles yielded areas of receiver operator characteristic curves (ROC) that distinguished differences among benign cancer-adjacent nuclei and GG-3 (ROC-AUC = 0.78); GG-4 (ROC-AUC = 0.86) and GG-5 (ROC-AUC = 0.88) with accuracies of 73%, 78% and 80% respectively. Applying PP plots generated from MLR models of GG 3, 4, and 5 nuclei clearly demonstrated marked heterogeneity within each of these three GG patterns. QNM signatures illustrate alterations in nuclei structure, based upon nuclear morphometry within each of these three GG patterns, and might signify potential variations in PCa disease risk of progression outcomes. In the future a modified system of Gleason grading that considers not only glandular architecture but also quantitative nuclear grade may ensure accuracy in prognosis.

P300 Modulates Nuclear Morphology in Prostate Cancer

Alterations in nuclear structure distinguish cancer cells from noncancer cells. These nuclear alterations can be translated into quantifiable features by digital image analysis in a process known as quantitative nuclear morphometry. Recently, quantitative nuclear morphometry has been shown to predict metastasis and biochemical recurrence of prostate cancer. However, little is known about the cellular mechanisms underlying these nuclear morphometric changes. Alterations of nuclear matrix proteins are frequently involved in changes of nuclear structure. A number of co-activators interact with these nuclear structure-related proteins, suggesting that they might be involved in quantitative nuclear morphometry changes. We have shown previously that the transcriptional co-activator p300 is involved in prostate cancer progression. However, the ability of a transcriptional regulator like p300 to modulate nuclear morphology has not been described previously. In the present study, we show that p300 expression in prostate cancer biopsy tissue from 95 patients correlates with quantifiable nuclear alterations. Moreover, we show that transfection of p300 into prostate cancer cells in culture induces quantifiable nuclear alterations, such as diameter, perimeter, and absorbance among others, as assessed by digital image analysis. These alterations correlate individually with aggressive features in prostate cancer, such as expression of the proliferation marker Ki-67 and extraprostatic extension of the tumor. Finally, we found that transfection of p300 into prostate cancer cells specifically increases mRNA and protein levels of nuclear matrix peptides lamins A and C, suggesting that these proteins mediate the p300-induced effects. These findings reveal a new insight into the transcriptional and structural regulation of prostate cancer.

Prediction of Pathological Stage in Patients with Clinical Stage T1c Prostate Cancer: The New Challenge

We developed an algorithm for predicting the likelihood of organ confined disease in patients with clinical stage T1c prostate cancer using biopsy pathology, computer assisted image analysis and serum prostate specific antigen (PSA). Of the 557 consecutive men enrolled in this study between October 1998 and January 2000 scheduled for radical prostatectomy at a single institution 386 (69%) presented with clinical stage T1c disease. Study exclusion criteria included neoadjuvant hormonal treatment with luteinizing hormone-releasing hormone, antiandrogen or 5alpha-reductase inhibitors. Preoperative serum, biopsy histology slides, clinical demographic information, prostatectomy pathology and prostate weight data were obtained. Biomarkers assessed included total PSA, complexed PSA, free PSA, the free-to-total PSA ratio, quantitative nuclear grade determined by image analysis, complexed PSA density, and biopsy Gleason grade and score. To determine patient specific quantitative nuclear grade values, images from approximately 125 cancer nuclei were captured per patient from the area of the biopsy section with the highest Gleason score. The variance in 60 nuclear size, shape and chromatin texture descriptors was calculated for each gallery of nuclei. Logistic regression was done to determine the most accurate combination of variables for predicting organ confined prostate cancer. Complete results and data were available on 255 of the 386 men (66%) with an average age plus or minus standard deviation of 58.8 +/- 6 years who had stage T1c disease, including 49 (19%) with pathologically nonorgan confined disease. Logistic regression analysis revealed that quantitative nuclear grade, biopsy Gleason score, total PSA, the calculated free-to-total PSA ratio, complexed PSA and complexed PSA density were univariately significant for predicting organ confined disease (p <0.05). On backward stepwise logistic regression only quantitative nuclear grade, complexed PSA density and Gleason score remained in a model yielding an area under the receiver operating characteristics curve of 82.4%. The quantitative nuclear grade biomarker was the strongest independent predictor of pathological stage in men with clinical stage T1c prostate cancer when combined with biopsy Gleason score and complexed PSA density data.

Quantitative nuclear grade (QNG): a new image analysis-based biomarker of clinically relevant nuclear structure alterations.

This review addresses the potential clinical value of using quantitative nuclear morphometry information derived from computer-assisted image analysis for cancer detection and predicting outcomes such as tumor stage, recurrence, and progression. Today's imaging technology uses sophisticated hardware platforms coupled with powerful and user-friendly software packages that are commercially available as complete image analysis systems. There are many different mathematically derived nuclear morphometric descriptors (NMD's) (i.e. texture features) that can be calculated by these image analysis systems, but for the most part, these NMD's quantify nuclear size, shape, DNA content (ploidy), and chromatin organization (i.e. texture, both Markovian and non-Markovian) parameters. We have utilized commercially available image analysis systems and the NMD's calculated by these systems to create a mathematical solution, termed quantitative nuclear grade (QNG), for making clinical, diagnostic, and prognostic outcome predictions in both prostate and bladder cancer. A separate computational model is calculated for each outcome of interest using well-characterized and robust training, testing, and validation patient sample sets that adequately represent the selected population and clinical dilemma. A specific QNG solution may be calculated either by non-parametric statistical methods or non-linear mathematics employed by artificial neural networks (ANNs). The QNG solution, a measure of genomic instability, provides a unique independent variable to be used alone or to be included in an algorithm to assess a specific clinical outcome. This approach of customization of the nuclear morphometric descriptor (NMD) information through the calculation of a QNG solution mathematically adjusts for redundancy of features and reduces the complexity of the inputs used to create decision support tools for patient disease management. J. Cell. Biochem. Suppl. 35:151-157, 2000.

NUCLEAR MORPHOMETRY FOR PREDICTING CERVICAL METASTATIC POTENTIAL IN LARYNGEAL SQUAMOUS-CELL CARCINOMA

Quantitative nuclear morphometry (QNM) provides a reliable, reproducible method for measuring the degree of nuclear pleomorphism, a qualitative parameter widely incorporated into histopathological grading schemes. Nuclear shape has successfully been shown to predict primary prostate adenocarcinoma metastatic potential. This investigation utilized QNM to measure the nuclear pleomorphism of 50 patients with primary laryngeal squamous cell carcinoma to determine whether this computer-assisted method could discriminate between those patients with and without cervical metastases. Nuclear cross-sectional area and nuclear roundness as determined by QNM could not discriminate between patients with and without cervical metastases, though significant alterations in nuclear morphology exist between normal laryngeal squamous epithelium and laryngeal squamous cell carcinoma.

The Use of Nuclear Morphometry in Predicting Recurrence of Transitional Cell Carcinoma

Despite apparently adequate surgical resection, metastases develop in about 50% of the patients who undergo radical cystectomy for invasive bladder cancer and they die of disease within 2 years of surgery. Recent reports have shown that combination chemotherapy with regimens including cisplatin results in significant improvement in time to progression and overall survival for patients with transitional cell carcinoma. We have used quantitative nuclear morphometry in an effort to predict which individual patients with muscle invasive transitional cell carcinoma are at increased risk for recurrence and in need of adjuvant therapy. A total of 14 patients with deeply invasive tumors without known metastases (tumors, nodes and metastasis stage P3A-B, N-, MO) who had not received perioperative radiation or systemic chemotherapy was studied. Of the patients 7 (group 1) had no evidence of disease with a mean followup of 41 months and 7 (group 2) had recurrent transitional cell carcinoma at a mean followup of 17 months. Both groups were similar with respect to age and could not be distinguished by standard pathological analysis of stage (p = 0.66) or grade (p = 0.99). A total of 150 tumor nuclei from each patient was digitized and analyzed by a high resolution quantitative morphometric imaging system, developed in this laboratory, to determine if nuclear shape descriptors could separate these 2 clinically distinct groups. A multivariate analysis combining 3 independent nuclear shape descriptors (average nuclear area, minimum feret-diameter ratio and kurtosis of feret-diameter ratio) was highly significant (p = 0.003) and separated patients in group 1 from group 2 without overlap. These encouraging results suggest that nuclear morphometry may be valuable in determining which patients are at increased risk of disease recurrence following radical cystectomy and, therefore, who should receive adjuvant chemotherapy.

Nasocytologic Examination of Wood Industry Workers

To establish a methodology for field examination for early diagnosis of ethmoidal cancer in workers in the furniture industry, two methods were compared, namely nasal rinsing and direct sample-taking from the middle meatus. The latter method proved superior, resulting in a high number of cylinder cells. This method was tried out in a field survey of 715 furniture workers. No tumours of precancerous changes were revealed. The investigation continues, by exposing the Syrian golden hamster to wood-dust and using, amongst other methods, quantitative nuclear morphometry for evaluation.