Intraobserver and interobserver variability in assessing the quality of cervical smears, as measured by the presence or absence of endocervical columnar cells and squamous metaplastic cells, was evaluated. In total, 180 cervical smears representing the most important cytologic diagnoses were anonymously rescreened twice by 19 observers with an interval of six months. An absence of endocervical columnar cells was proven to correlate with a high percentage of false-negative diagnoses. Intraobserver agreement on the presence or absence of endocervical columnar cells was 85.7% between the two screenings. A predictive value of 57.7% was found for a negative scoring (absence of these cells) while the predictive value of a positive scoring (presence of endocervical cells) was 87.3%. Of the observer scorings, 83.9% concurred with the final diagnosis; there was no significant correlation between that concurrence and the number of years of experience in cytopathology of the observer. For squamous and squamous metaplastic cells in the cervical smear the predictive value of a negative scoring (absence) was only 20.6%. Compared to the final diagnosis, 69.5% of these scorings matched. A significant and relatively high correlation with the experience of the observer was found for the scoring for the presence of metaplastic cells. Even though the predictive values of these quality scorings were relatively low a significantly higher risk for false diagnoses was established when negative scorings were given. It is therefore advisable to have smears with negative scorings for endocervical columnar cells and squamous metaplastic cells always rescreened by another observer.
"However, traditional histology remains a subjective technique, with significant problems often encountered. These include missed lesions and unsatisfactory levels of inter- and intra-observer agreement [5-10]. Alternative techniques have been employed to facilitate faster intra-operative diagnosis of sentinel nodes, including imprint cytology [11,12], and frozen section analysis [13,14]. "
[Show abstract][Hide abstract] ABSTRACT: Histopathologic evaluation of surgical specimens is a well established technique for disease identification, and has remained relatively unchanged since its clinical introduction. Although it is essential for clinical investigation, histopathologic identification of tissues remains a time consuming and subjective technique, with unsatisfactory levels of inter- and intra-observer discrepancy. A novel approach for histological recognition is to use Fourier Transform Infrared (FT-IR) micro-spectroscopy. This non-destructive optical technique can provide a rapid measurement of sample biochemistry and identify variations that occur between healthy and diseased tissues. The advantage of this method is that it is objective and provides reproducible diagnosis, independent of fatigue, experience and inter-observer variability.
We report a method for analysing excised lymph nodes that is based on spectral pathology. In spectral pathology, an unstained (fixed or snap frozen) tissue section is interrogated by a beam of infrared light that samples pixels of 25 mum x 25 mum in size. This beam is rastered over the sample, and up to 100,000 complete infrared spectra are acquired for a given tissue sample. These spectra are subsequently analysed by a diagnostic computer algorithm that is trained by correlating spectral and histopathological features.
We illustrate the ability of infrared micro-spectral imaging, coupled with completely unsupervised methods of multivariate statistical analysis, to accurately reproduce the histological architecture of axillary lymph nodes. By correlating spectral and histopathological features, a diagnostic algorithm was trained that allowed both accurate and rapid classification of benign and malignant tissues composed within different lymph nodes. This approach was successfully applied to both deparaffinised and frozen tissues and indicates that both intra-operative and more conventional surgical specimens can be diagnosed by this technique.
This paper provides strong evidence that automated diagnosis by means of infrared micro-spectral imaging is possible. Recent investigations within the author's laboratory upon lymph nodes have also revealed that cancers from different primary tumours provide distinctly different spectral signatures. Thus poorly differentiated and hard-to-determine cases of metastatic invasion, such as micrometastases, may additionally be identified by this technique. Finally, we differentiate benign and malignant tissues composed within axillary lymph nodes by completely automated methods of spectral analysis.
"of which histological evaluation and morphologic grading are the most important. However, these methods are based on subjective evaluations which often result in considerable inter-and intra-observer variation    . Therefore, image analysis methods have been developed in an attempt to obtain more objective diagnosis. "
[Show abstract][Hide abstract] ABSTRACT: A large body of the published literature in nuclear image analysis do not evaluate their findings on an independent data set. Hence, if several features are evaluated on a limited data set over-optimistic results are easily achieved. In order to find features that separate different outcome classes of interest, statistical evaluation of the nuclear features must be performed. Furthermore, to classify an unknown sample using image analysis, a classification rule must be designed and evaluated. Unfortunately, statistical evaluation methods used in the literature of nuclear image analysis are often inappropriate. The present article discusses some of the difficulties in statistical evaluation of nuclear image analysis, and a study of cervical cancer is presented in order to illustrate the problems. In conclusion, some of the most severe errors in nuclear image analysis occur in analysis of a large feature set, including few patients, without confirming the results on an independent data set. To select features, Bonferroni correction for multiple test is recommended, together with a standard feature set selection method. Furthermore, we consider that the minimum requirement of performing statistical evaluation in nuclear image analysis is confirmation of the results on an independent data set. We suggest that a consensus of how to perform evaluation of diagnostic and prognostic features is necessary, in order to develop reliable tools for clinical use, based on nuclear image analysis.
Analytical cellular pathology: the journal of the European Society for Analytical Cellular Pathology 02/1998; 16(2):63-82. DOI:10.1155/1998/436382
[Show abstract][Hide abstract] ABSTRACT: Objective: The recently developed software (CONQUISTADOR), capable of computing all intralaboratory and interlaboratory quality control (QC) indicators, was used to evaluate the diagnostic agreement among 4 cytology laboratories participating in the LAMS Study. Study Design: The study was an interlaboratory exchange of specially designed 5 slide sets, each comprising 20 (conventional cytology) slides. At the first step, 80 slides (with "clear-cut" cases) were divided into four sets (A, B, C, D) of 20 specimens, each including inadequate and negative cases as well as in different proportions of all diagnostic TBS 2001 categories. In the second round, a fifth set (E) of 20 slides ("difficult cases") was designed, with all diagnostic categories, ASC and AGC included. Common measures of reproducibility (κ and weighted κ), accuracy (SE, SP, PPV, NPV) and 3 indices of diagnostic variability were calculated for sets A-D and set E, separately. Results: For the 5 slide sets together, the weighted κ was 0.8 (95% CI 0.76-0.85), which is the lower limit of the "almost perfect" ranking of κ statistics, indicating an excellent interlaboratory agreement. The interlaboratory reproducibility was lower only for the difficult set (E). Similarly, the sensitivity for set E (70.0%) was lower than that (92.1%) for sets A-D. The diagnostic variability indices were not substantially different between the difficult (set E) and clear-cut (sets A-D) cases. Conclusion: High interlaboratory reproducibility was obtained for sets A-D ("clear-cut" cases), while more interlaboratory variation was evident in the difficult samples. The new CONQUISTADOR software is a valuable tool in calculating the indicators needed in this intralaboratory and interlaboratory QC.
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