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A Computer-aided Study on the Tissue Changes in Oral Keratoses and Lichen Planus and an Analysis of Case Groupings by Subjective and Objective Criteria

机译:口腔角化病和扁平苔藓组织变化的计算机辅助研究并通过主观和客观标准对病例分组进行分析

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摘要

In a retrospective survey of 235 cases in which the diagnosis on biopsy was lichen planus, keratosis or leukoplakia, the histological features were re-assessed in as objective a manner as possible. For each case, the tissue changes were recorded under 37 headings, without any attempt at interpretation. The information was then transferred to punched cards, and used for various types of computer-aided analysis. Firstly, the frequency with which each tissue change occurred was assessed for each diagnostic group, and the data presented also show how often each change occurred in those cases that subsequently developed a carcinoma.Re-analysis of the cases on the basis of short lists of tissue changes that were thought to characterize each diagnostic category showed, as expected, that many other factors must have played a part in the original diagnosis.Analysis of the histological findings on an objective basis, using a cluster analysis programme, provided an encouraging degree of separation into the diagnostic groups. When a number of known carcinoma cases were included in the cluster analysis as “markers”, a small number of leukoplakia and keratosis cases were placed by the computer into the same cluster as these “markers”. Of the original 187 cases originally diagnosed as leukoplakia or keratosis, 4.8% are known to have developed a carcinoma, but of the 11 cases the computer placed in the same cluster as the “marker” cases of carcinoma, 36% have subsequently developed carcinoma. Thus, in the cluster analyses, the computer is tending to “recognize” those cases that later developed carcinoma, and to separate them from the bulk of the cases in which malignant change has not occurred.
机译:在对235例活检诊断为扁平苔藓,角化病或白斑的病例进行的回顾性调查中,尽可能客观地重新评估了组织学特征。对于每种情况,组织变化均记录在37个标题下,而无需进行任何解释。然后将信息转移到打孔卡上,并用于各种类型的计算机辅助分析。首先,对每个诊断组评估每种组织变化发生的频率,并且所提供的数据还显示了随后发生癌症的那些病例中每种变化发生的频率。如预期的那样,被认为是每个诊断类别的特征的组织变化表明,许多其他因素也必须在原始诊断中起作用。使用聚类分析程序客观地分析组织学发现,提供了令人鼓舞的程度。分成诊断组。当在聚类分析中包括许多已知的癌病例作为“标记”时,计算机会将少量白斑和角化病病例与这些“标记”放置在同一聚类中。在最初被诊断为白斑或角化病的最初187例病例中,已知有4.8%患了癌,但是在11例与“标记”癌病例放在同一集群中的计算机中,有36%随后患了癌。因此,在聚类分析中,计算机倾向于“识别”后来发展为癌症的病例,并将其与尚未发生恶性变化的大部分病例区分开。

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