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Biomedical subjects

K Yogesan

Publications and source records attributed to K Yogesan.

21 records · Page 2Linked to original sources

Histopathological grading and DNA ploidy as prognostic markers in metastatic prostatic cancer.

The present study compares the prognostic potential of tumour grade and DNA ploidy status in patients with advanced-stage prostatic cancer. Two outcome groups were selected on the basis of time to progression and survival after orchiectomy. A poor-outcome group consisted of 32 therapy-resistant patients who experienced disease progression during the first year after orchiectomy and subsequently death due to prostatic cancer during the following year. A good-outcome group consisted of 27 therapy-responsive patients who showed disease regression and no signs of progression during a 3 year follow-up. The primary tumours were graded twice according to WHO and Gleason classification systems by two pathologists. Final agreement between the pathologists was obtained after a consensus meeting. The analysis revealed no prognostic importance of the two histological classification systems (P = 0.62 and P = 0.70) and disclosed weak inter- and intra-observer reproducibility (kappa < 0.70). DNA ploidy analyses were performed by image cytometry on formalin-fixed, paraffin-embedded samples of the primary tumours. Overall, 48% of the tumours were diploid, 20% tetraploid and 32% anueploid. DNA ploidy status did not discriminate between the two outcome groups (P = 0.46). Histological grade and DNA ploidy showed no prognostic importance in patients with prostatic cancer and skeletal metastases.

Adult↗

Ultrastructural texture analysis as a diagnostic tool in mouse liver carcinogenesis.

Nuclear texture, which reflects the overall structure of the chromatin, may be used to detect early as well as later stages of malignancy. In this study, texture analysis was applied to four groups of liver cells in mice: normal and regenerating liver, hyperplastic nodules, and hepatocellular carcinomas. The best discriminating set of features was selected based on a training data set. The model was then tested on an independent series of 10 hyperplastic nodules and 6 hepatocellular carcinomas. A correct classification rate of 95% was obtained on the training data set and 100% accuracy was obtained on the test set. This kind of image analysis technique offers an opportunity to identify and describe the nuclear changes related to carcinogenesis, and the present results demonstrate the possible use of digital texture analysis as a diagnostic aid in tumor pathology.

Animals↗