Prognosis, Recurrences and Metastases Correlated to Histologic Cell Type in Carcinoma of the Uterine Cervix

Johansson, Ola; Johnsson, J. E.; Lindberg, Lars Göran; Sydsjö, A.

doi:10.3109/00016347609156923

Cited by 15 publications

(3 citation statements)

References 17 publications

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“…Johansson et aI. (8) were unable to show any obvious differences in tumour-reaction to radiation therapy in a tumour-material from 1969-1970. It was considered to be a representative sample out of the present tumour-material from 1963-1972.…”

Section: Pe/vic Recurrence Of Carcinoma 229mentioning

confidence: 87%

Hysterectomy in Central Recurrence of Carcinoma of The Uterine Cervix

Björnståhl

Johnsson

Lindberg

1977

Acta Obstet Gynecol Scand

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Section: Pe/vic Recurrence Of Carcinoma 229mentioning

confidence: 87%

Hysterectomy in Central Recurrence of Carcinoma of The Uterine Cervix

Björnståhl

Johnsson

Lindberg

1977

Acta Obstet Gynecol Scand

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“…The reproducibility of some grading systems can be as low as 30-40% [5,45]. Furthermore, in some tumours the correlation between clinical outcome and grading before treatment can be low or none [20,44]. However, grading of bladder carcinoma seems to have considerable prognostic potential despite the incongruity between the various systems used [5,30,33].…”

Section: Discussionmentioning

confidence: 99%

Image Analysis Based Grading of Bladder Carcinoma. Comparison of Object, Texture and Graph Based Methods and Their Reproducibility

Choi

Jarkrans

Bengtsson

et al. 1997

Analytical Cellular Pathology

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The possibility that computerized image analysis could increase the reproducibility of grading of bladder carcinoma as compared to conventional subjective grading made by pathologists was investigated. Object, texture and graph based analysis were carried out from Feulgen stained histological tissue sections. The object based features were extracted from gray scale images, binary images obtained by thresholding the nuclei and several other images derived through image processing operations. The textural features were based on the spatial gray‐tone co‐occurrence probability matrices and the graph based features were extracted from the minimum spanning trees connecting all nuclei. The large numbers of extracted features were evaluated in relation to subjective grading and to factors related to prognosis using multivariate statistical methods and multilayer backpropagation neural networks. All the methods were originally developed and tested on material from one patient and then tested for reproducibility on entirely different patient material. The results indicate reasonably good reproducibility for the best sets of features. In addition, image analysis based grading showed almost identical correlation to mitotic density and expression of p53 protein as subjective grading. It should thus be possible to use this kind of image analysis as a prognostic tool for bladder carcinoma.

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“…Cervical cancer is the most common gynecologic malignancy in developing countries and is the leading cause of gynecological cancer death, with approximately 275,000 deaths in 2008 each year ( Ferlay et al, 2010 ). The most common metastatic sites in descending order are lung, bone and liver ( Berek and Hacker, 2000 ; Johansson et al, 1976 ). Skin metastases are a rare event in patients with cervical cancer.…”

Section: Introductionmentioning

confidence: 99%