Literature DB >> 17521881

Evaluation of the invasion front pattern of squamous cell cervical carcinoma by measuring classical and discrete compactness.

Jens Einenkel1, Ulf-Dietrich Braumann, Lars-Christian Horn, Nadine Pannicke, Jens-Peer Kuska, Alexander Schütz, Bettina Hentschel, Michael Höckel.   

Abstract

The invasion front pattern of squamous cell carcinoma (SCC) is a conspicuous histological phenomenon, which is assessed without precise criteria. The current study was performed to introduce the classical (C(C)) and discrete compactness (C(D)) as new morphometric parameters for quantification of this pattern. A retrospective analysis of 76 surgically treated patients with cervical carcinoma was conducted and the pattern of invasion was qualitatively classified as closed, finger-like or diffuse, respectively, by two pathologists. After digitization of the histological slides with a field of view of 10.4 mm x 8.3mm, tumor areas were labeled and C(C) and C(D) were computed based on the drawings (binary images). Additionally, intraindividual variation of compactness was evaluated for 12 selected tumors. The qualitative pattern assessment by the pathologists was moderately reproducible with an interobserver agreement of 72% and a kappa coefficient of 0.44. The values of C(C) and C(D) referring to the invasion front patterns assigned by both pathologists were significantly different between the three classified groups (p< or =0.01 and p< or =0.0001), so that, both theoretically and in practice, compactness regards the same morphological feature. In due consideration of the analysis of the area under the ROC (receiver operating characteristic) curves and the variation coefficient of different tumor regions, C(D) is more suitable for practical use than C(C). Tumors with a microscopic invasion into the parametria and with lymph-vascular space invasion were found to have a lower value of C(D), which indicates a more diffuse pattern of invasion (p=0.028 and p=0.033). We conclude that the discrete compactness C(D) is a new and reproducible parameter for a computer assisted quantification of the invasion front pattern and, thus, defines a further phenotypic feature of SCC of the uterine cervix.

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Year:  2007        PMID: 17521881     DOI: 10.1016/j.compmedimag.2007.03.004

Source DB:  PubMed          Journal:  Comput Med Imaging Graph        ISSN: 0895-6111            Impact factor:   4.790


  4 in total

1.  Neuromorphometry of primary brain tumors by magnetic resonance imaging.

Authors:  Nidiyare Hevia-Montiel; Pedro I Rodriguez-Perez; Paul J Lamothe-Molina; Alfonso Arellano-Reynoso; Ernesto Bribiesca; Marco A Alegria-Loyola
Journal:  J Med Imaging (Bellingham)       Date:  2015-05-12

2.  Radiomics predicts survival of patients with advanced non-small cell lung cancer undergoing PD-1 blockade using Nivolumab.

Authors:  Valerio Nardone; Paolo Tini; Pierpaolo Pastina; Cirino Botta; Alfonso Reginelli; Salvatore Francesco Carbone; Rocco Giannicola; Grazia Calabrese; Carmela Tebala; Cesare Guida; Aldo Giudice; Vito Barbieri; Pierfrancesco Tassone; Pierosandro Tagliaferri; Salvatore Cappabianca; Rosanna Capasso; Amalia Luce; Michele Caraglia; Maria Antonietta Mazzei; Luigi Pirtoli; Pierpaolo Correale
Journal:  Oncol Lett       Date:  2019-12-16       Impact factor: 2.967

3.  Quantitative radiomic model for predicting malignancy of small solid pulmonary nodules detected by low-dose CT screening.

Authors:  Liting Mao; Huan Chen; Mingzhu Liang; Kunwei Li; Jiebing Gao; Peixin Qin; Xianglian Ding; Xin Li; Xueguo Liu
Journal:  Quant Imaging Med Surg       Date:  2019-02

4.  Radiomics in lung cancer for oncologists.

Authors:  Carolina de la Pinta; Nuria Barrios-Campo; David Sevillano
Journal:  J Clin Transl Res       Date:  2020-09-02
  4 in total

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